Detecting hazards based on disparity maps using machine learning for autonomous machine systems and applications

ABSTRACT

In various examples, systems and methods for machine learning based hazard detection for autonomous machine applications using stereo disparity are presented. Disparity between a stereo pair of images is used to generate a path disparity model. Using the path disparity model, a machine learning model can recognize when a pixel in the first image corresponds to a pixel in the second image even though the pixel in the two images does not have identical characteristics. Similarities in extracted feature vectors can be computed and represented by a vector similarity metric that is input to a machine learning classifier, along with feature information extracted from the stereo image pair, to differentiate hazard pixels from non-hazard pixels. In some embodiments, a V-space disparity map, where a first axis corresponds to disparity values and the second axis corresponds to pixel rows, may be used to simplify estimation of the path disparity model.

BACKGROUND

The ability to safely identify and navigate around hazards on a roadwayis a critical task for any autonomous or semi-autonomous driving system.For example, an adequate hazard detection system must be robust todifferent types of hazards and include a high capacity to detect smallhazards at a distance to allow an ego-vehicle enough time to avoid ahazard. While some conventional systems are able to detect roadwayhazards, these systems require extensive training, rely on inaccurateassumptions, and/or are very expensive to implement.

Parallax-based image processing represents one existing technologycurrently used for hazard detection. The parallax-based approach usesinformation derived from two image frames captured at different times bya single camera. For the two image frames to have sufficient parallaxinformation to detect hazard, a vehicle would need to travel asufficient distance with sufficient time latency between capturing thetwo image frames. Otherwise the parallax information could beinsufficient to be used in the hazard detection with sufficiently highconfidence. The Parallax-based technique is also highly dependent on aflat road assumption (that an overlapping road region between the twoinput image frames is a flat plane) which is not always the case in realsituations.

Another existing hazard detection technology leverages a Deep NeuralNetwork (DNN) inference engine. The DNN is trained on the appearance andshapes of hazards to detect when a hazard appears in a captured image.However, given the wide variety of hazards that might be present on aroad surface, large amounts of training data are needed, and obtainingsuch training data is not trivial due to the unknown and unlimited typesof potential hazards.

LiDAR-sensor based approaches represent another existing technologycurrently used for hazard detection. The LiDAR sensor senses the 3Dsurroundings of the ego-machine to produce, e.g., a point cloud, that itcan actively use to detect the existence of a hazard above the roadsurface. However, LiDAR sensors are expensive and may not beeconomically practical for autonomous vehicle applications, such asconsumer ego-vehicles, for example. Moreover, the density of the pointcloud generated by LiDAR reduces with range, which makes it difficult todetect small hazards and/or estimate the hazard size, especially itsheight dimension, at long range.

While some conventional systems may combine the above approaches, thesecombinations do not overcome many of the individual shortcomings ofthese conventional solutions.

SUMMARY

Embodiments of the present disclosure relate to machine learning basedhazard detection using stereo disparity for autonomous machine systemsand applications. Systems and methods are disclosed that assist anego-machine in detecting hazards within its path of travel.

In contrast to conventional systems, such as those described above, thesystems and methods presented in this disclosure use disparityinformation between a stereo pair of images to generate a disparitymodel for the road or path on which an ego-machine is traveling. Usingthe path disparity model, a machine learning model can recognize when apixel in the first image corresponds to a particular pixel in the secondimage (in the sense that both feature vectors represent the samephysical real-life feature) even though the pixels as they appear in thetwo images do not have identical characteristics. Such recognition incorrespondence may be measured by a similarity metric between extractedfeature vectors of the corresponding pair of pixels from the stereoimage pair. Given the path disparity model, a correct correspondenceoffset can be computed for each pixel of the stereo image pair forpixels representing a position on the path surface, but such acorrespondence will not be present for a pixel comprising a hazard. Ahazard will produce disparity that deviates from the disparity predictedfrom the path disparity model. Pixel correspondence information fromdisparity may be further combined with feature vectors computed by amachine learning model, and may help distinguish hazard pixels from roadpixels. Substantial similarity can be expected between a feature vectorextracted from the first image and a corresponding feature vectorextracted from the second image for road pixels, and substantialdissimilarity expected for hazard pixels (e.g., pixels that correspondto a hazard above or below a road surface). Similarity can be computedand represented, in some embodiments, by a vector similarity metric thatis input to a machine learning classifier, along with the featureinformation extracted from the stereo image pair, to differentiate“hazard” pixels from “non-hazard” pixels. The techniques disclosedherein provide an ego-machine with effective and robust hazard detectionbecause the path disparity model is used to reveal unexpected shifts infeatures between stereo image pairs, and correspondence information usedto augment information extracted from the stereo image pairs.

In some embodiments, blockwise division is used to subdivide thedisparity map into a plurality of blocks comprising smaller disparitymaps. Disparity of the road surface within each block is considerablyless than the disparity of the entire road surface as a whole, so thatwithin each block disparity caused by hazards is more readilydistinguishable. Isolated hazard pixels, absent other nearby hazardpixels, may sometimes represent false positives resulting from systemnoise. These false positives tend to be distributed randomly andsparsely in the image, while true hazard pixels are more denselyclustered together. Therefore, in embodiments, a clustering algorithm isapplied to those pixels, e.g., in image space, to remove false positiveand define clusters of hazard pixels that correlate to real hazardobjects on the path of the ego-machine. After hazard detection isperformed within each block, the results can then be correlated back toan image of the path surface, and the hazard locations may be used toaid in navigation or control of the ego-machine.

BRIEF DESCRIPTION OF THE DRAWINGS

The present systems and methods for machine learning based hazarddetection using stereo disparity for autonomous machine systems andapplications are described in detail below with reference to theattached drawing figures, wherein:

FIG. 1 is an illustration of an example data flow diagram for a learningbased hazard detection system of an ego-machine, in accordance with someembodiments of the present disclosure;

FIG. 2 is an illustration of an example of an overlapping field of view(FOV) for a sensor pair, in accordance with some embodiments of thepresent disclosure;

FIG. 3 is an illustration of an example stereo disparity machinelearning hazard detector, in accordance with some embodiments of thepresent disclosure;

FIG. 4 is an illustration of an example discontinuity in disparityvalues for detecting a roadway hazard, in accordance with someembodiments of the present disclosure;

FIG. 5 is an illustration of an example flow diagram showing a methodfor machine learning based hazard detection using stereo disparity, inaccordance with some embodiments of the present disclosure;

FIG. 6 is an illustration of an example of disparity map generation formachine learning based hazard detection using stereo disparity, inaccordance with some embodiments of the present disclosure;

FIG. 7 is an illustration of an example of hazard element clustering, inaccordance with some embodiments of the present disclosure;

FIG. 8 is an illustration of an example flow diagram showing a methodfor training a model for machine learning based hazard detection usingstereo disparity, in accordance with some embodiments of the presentdisclosure;

FIG. 9A is an illustration of an example autonomous vehicle, inaccordance with some embodiments of the present disclosure;

FIG. 9B is an example of camera locations and fields of view for theexample autonomous vehicle of FIG. 9A, in accordance with someembodiments of the present disclosure;

FIG. 9C is a block diagram of an example system architecture for theexample autonomous vehicle of FIG. 9A, in accordance with someembodiments of the present disclosure;

FIG. 9D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle of FIG. 9A, in accordancewith some embodiments of the present disclosure;

FIG. 10 is a block diagram of an example computing device suitable foruse in implementing some embodiments of the present disclosure; and

FIG. 11 is a block diagram of an example data center suitable for use inimplementing some embodiments of the present disclosure.

DETAILED DESCRIPTION

Systems and methods are disclosed related to machine learning basedhazard detection using stereo disparity for autonomous machine systemsand applications. Although the present disclosure may be described withrespect to an example autonomous vehicle 900 (alternatively referred toherein as “vehicle 900” or “ego-machine 900,” an example of which isdescribed with respect to FIGS. 9A-9D), this is not intended to belimiting. For example, the systems and methods described herein may beused by, without limitation, non-autonomous vehicles, semi-autonomousvehicles (e.g., in one or more advanced driver assistance systems(ADAS)), piloted and un-piloted robots or robotic platforms, warehousevehicles, off-road vehicles, vehicles coupled to one or more trailers,flying vessels, boats, shuttles, emergency response vehicles,motorcycles, electric or motorized bicycles, aircraft, constructionvehicles, underwater craft, drones, and/or other vehicle types. Inaddition, although the present disclosure may be described with respectto detecting hazardous objects on an ego-vehicle's path of travel, thisis not intended to be limiting, and the systems and methods describedherein may be used in augmented reality, virtual reality, mixed reality,robotics, security and surveillance, digital twin and other simulationapplications, autonomous or semi-autonomous machine applications, and/orany other technology spaces where hazard or object detection may beused.

The systems and methods described herein may be used by, withoutlimitation, non-autonomous vehicles, semi-autonomous vehicles (e.g., inone or more adaptive driver assistance systems (ADAS)), piloted andun-piloted robots or robotic platforms, warehouse vehicles, off-roadvehicles, vehicles coupled to one or more trailers, flying vessels,boats, shuttles, emergency response vehicles, motorcycles, electric ormotorized bicycles, aircraft, construction vehicles, underwater craft,drones, and/or other vehicle types. Further, the systems and methodsdescribed herein may be used for a variety of purposes, by way ofexample and without limitation, for machine control, machine locomotion,machine driving, synthetic data generation, model training, perception,augmented reality, virtual reality, mixed reality, robotics, securityand surveillance, autonomous or semi-autonomous machine applications,deep learning, environment simulation, object simulation or digitaltwinning, data center processing, conversational AI, light transportsimulation (e.g., ray-tracing, path tracing, etc.), collaborativecontent creation for 3D assets, cloud computing and/or any othersuitable applications.

Disclosed embodiments may be comprised in a variety of different systemssuch as automotive systems (e.g., a control system for an autonomous orsemi-autonomous machine, a perception system for an autonomous orsemi-autonomous machine), systems implemented using a robot, aerialsystems, medial systems, boating systems, smart area monitoring systems,systems for performing deep learning operations, systems for performingsimulation operations including digital twinning, systems implementedusing an edge device, systems incorporating one or more virtual machines(VMs), systems for performing synthetic data generation operations,systems implemented at least partially in a data center, systems forperforming conversational AI operations, systems for performing lighttransport simulation, systems for performing collaborative contentcreation for 3D assets, systems implemented at least partially usingcloud computing resources, and/or other types of systems.

More specifically, the systems and methods presented in this disclosureassist an ego-machine in detecting hazards within its path of travel.Such hazards may come in the form of foreign material on the roadway,defects in the surface of the roadway, or other vehicles, trafficcontrol objects, wild or free-range animals, and/or pedestrians, thatcould be present on what is otherwise defined as a permitted path oftravel for the ego-machine. Failure to avoid these hazards can result indamage or injury and, as a result, an autonomous machine orsemi-autonomous ego-machine operating on public streets and highways isexpected to detect and avoid such hazards by either navigating aroundthe hazard, or stopping before the hazard is reached. The ego-machine isexpected to employ hazard detection that is robust to different types ofhazards and that accurately detects small hazards in the farawaydistance well before they are reached by the ego-machine.

In contrast to these existing hazard detection technologies, the systemsand methods presented in this disclosure use disparity between a stereopair of images to generate a baseline road (or path) disparity model.Using the path disparity model, a determination is made to determine iffeatures appearing in one image of the stereo image pair appear in theposition predicted by the path disparity model in the other image of thestereo image pair. An offset from the predicted location indicates thata detected feature is not a component of the road, and thereforepossibly represents a hazard present on the surface of the road. Thisoffset may also be referred to herein as a “feature offset.” A magnitudeor other quantitative value for the feature offset can be computed andinput to a machine learning classifier, along with feature informationextracted from the stereo image pair, to differentiate hazard pixelsfrom non-hazard pixels. These techniques provide an ego-machine witheffective and robust hazard detection because they leverage a road (orpath) disparity model to detect shifts in feature positions betweenstereo image pairs and use the feature offset to augment informationdirectly extracted from the images for hazard detection. The pathdisparity model provides reliable and precise information about thedisparity on the road so that hazard pixels can be robustly recognizedfrom the detected feature offsets.

Disparity, or binocular disparity, is a geometric term used in the fieldof computer vision that refers to the difference in image pixel locationof an object as seen in left and right stereo images captured by acamera pair. The numeric value of a computed disparity also reflectsobject depth away from the cameras, wherein the bigger the disparity,the longer the object. For example, a pair of image sensors O, O′, maybe set up on an ego-machine with their respective optical axes alignedin parallel to capture a forward facing stereo image pair of the path ofthe ego-machine. As an example, an object point X observed by the pairof sensors will have a pixel location at (x, y) in the image captured byO, and pixel location (x′, y′) in the image capture by O′. The imagescan be rectified to where the image rows are aligned such that y=y′. Thedisparity, d, for that pixel is then defined by the pixel locationdifference: d=x−x′. Moreover, according to the similar triangleprinciple, the disparity is proportional to sensor focal length, f, andthe baseline length, b, between the two sensors, and is inverselyproportional to the object depth, Z, which can be expressed as: d=b*f/Z.As such, a disparity map for the image pair is constructed in which eachelement (e.g., each pixel) of the disparity map represents a disparityfor a corresponding element of the image captured by O. The resultingdisparity map may be of the same size in terms of rows and columns asthe captured image and may be referred to, e.g., as an image spacedisparity map.

Given an ideal road surface that is flat and parallel to the row of thestereo camera system, the disparity, d, of a point on the road can becomputed based on its image row: d=b*f/Z=b*(r−r0)/H where d is thedisparity at row r, r0 is the row of a principal point, H is the sensorheight above the road, and b is the baseline length between the sensorsof the sensor paid. The magnitude of a road disparity is linearlycorrelated by image row. That is, the closer the image row underconsideration is to the image bottom, the bigger the disparity willappear.

However, the disparity information in the disparity map may be lessaccurate or precise than desirable due to a variety of noise sourcesduring the generation process, including sensor calibration and roadand/or hazard textures. The techniques in the present disclosuretherefore include computing a pattern of road disparity and, from that,building a mathematical path disparity model. For example, a linearmodel (e.g., a line fitting algorithm) can be applied to describe theroad disparity in the disparity map: d=f(r,c)=f(r), where d is thedisparity, and f is a function with image row(r), image column (c) asvariables. In this way, hazard detection can be accomplished byestimating the path disparity model and identifying offsets in theposition of features between the stereo image pairs that do not conformto the path disparity model.

In some embodiments, as an initial operation to generate a disparity mapfrom the stereo images, stereo rectification may be performed to convertthe original pair of images to a pair of rectified images havingcorrespondences between their respective image rows so that the searchspace for computing disparity values can be greatly reduced. There areseveral known algorithms that may be used for computing disparityvalues. In one non-limiting embodiment, Semi-Global Matching (SGM) isapplied to compute a globally consistent disparity map. Using the SGMalgorithm, disparity for each individual pixel is computed based on amatching cost (such as a normalized cross correlation, for example), anda global optimization performed on those computed disparity valued baseon a disparity smoothness parameter.

Moreover, in some embodiments, one or more filters may be applied to thecaptured image pair to constrain processing to a defined region ofinterest (ROI) that includes the path of the ego-machine. Hazards thatpose a risk for the ego-machine include those that exist on the forwardportion of the path that the ego-machine is traveling toward (that is,the drivable region of the path as shown in the captured images or othersensor data representations). In some embodiments, the detection ROI canbe extracted using freespace detection, lane detection, or other knowntechniques—e.g., one or more computer vision algorithms, machinelearning models, neural networks, and/or the like trained or programmedto identify drivable freespace, lanes, road boundaries, etc.Constraining hazard detection related processing to the ROI avoids wasteof computational resources on non-relevant regions of the capturedimages, thereby leading to more accurate hazard detection, reducednoise, and reduced runtime.

In some embodiments, blockwise division is used to subdivide thedisparity map into a plurality of smaller disparity maps, eachcorresponding to a block of pixels of the disparity map. Typically, aroad traveled by an ego-machine is not a flat plane. Rather, it can beexpected to slope to either side (e.g., to allow for rain runoff), andhave a surface that is not perfectly parallel to the row axis of thecaptured images. Both of these characteristics contribute to greaterroad surface disparity. By subdividing and treating each of these blocksas an individual processing unit for hazard detection, the disparity ofthe road surface itself within each block is considerably less than ifthe disparity of the entire road surface as captured by the stereoimages were considered. After hazard detection is performed within eachblock, the results can then be correlated back to an image of the roadsurface within the ROI.

In some embodiments, the dimensions of each block may not be uniform,but may vary as a function of which image rows they comprise. That is,blocks capturing parts of the road or path that are nearer theego-machine or the sensors thereof (and thus appear towards the bottomof the images) may comprise more image rows than those capturing partsof the road that are farther from the ego-machine (and thus appeartowards the top of the images). In some embodiments, the number of imagerows within a block may be determined based on detecting a vanishingpoint where the viewable segment of the road ends, the mounting positionof the sensors, and/or having blocks approximately capture equivalentroad surface areas.

In some embodiments, to simplify estimation of the path disparity model,the image space disparity map is transformed to an updated (or “V”)disparity map. That is, in an image space disparity map, the pixelcolumns are represented on a first axis (which may referred to at the Uaxis) and pixel rows are represented on the second axis (which may bereferred to as the V axis) and a disparity value at coordinates (u,v) ofthe disparity map may indicate a disparity associated with an imagepixel at coordinates (x,y) of the image as captured by one of thesensors (e.g., either sensor O or O′). In V-disparity space a first axisof the disparity map instead corresponds to disparity values while thesecond axis again corresponds to pixel rows (the V axis). Each of theone or more elements in the V-disparity map indicates a count ofdisparity elements in the row of the original image space disparity map.

The image space disparity map appearing within each of the block isaccordingly remapped to V-disparity space to generate a V-Disparity mapfor each respective block. With this mapping, the computed disparityvalues of the road surface as it appears in a V-Disparity map becomes asmooth line, or even a straight line if the road surface is flat andparallel to the image row. Therefore, a linear model such as a linefitting algorithm may be applied to derive an estimated path disparitymodel. As an example, in one embodiment, a Random Sample Consensus(RANSAC) line fitting algorithm is applied to the V-Disparity map foreach respective block to find a straight line model of the roaddisparity within each block.

In one embodiment, a first set of one or more feature vectors iscomputed for features extracted from the first image of the stereo imagepair, and a second set of one or more features is computed for featuresextracted from the second image of the stereo image pair. For example, aDeep Neural Network (DNN) model may be used to extract a feature map ofthe first image (for example, the left image), and extract a feature mapof the second image (for example, the right image). In this way, the DNNcomputes feature vectors for one or more pixels (e.g., each pixel) ofthe respective first and second images. Those pixels corresponding tohazards appearing in the stereo image pair are expected have a verydifferent texture and pattern as compared to pixels corresponding topixels comprising the surface of the road. These differences can becaptured and described by the feature vectors computed by the DNN model,which may be trained to recognize such semantic pattern and textureinformation, and form a first part of the classification informationcarried by a feature vector to differential a hazard pixel from a roadpixel.

Moreover, the DNN model through machine learning can recognize whichpixel in the first image corresponds to a pixel in the second image (inthe sense that both feature vectors represent the same physicalreal-life feature) even though the pixel in the two images do not haveidentical characteristics. For example, because the stereo sensors areoffset, one sensor will receive light reflecting off the surface of theroad or hazard at a slightly different angle that the other sensor,creating differences in observed luminosity between the two sensors. TheDNN model may recognize two pixels as a corresponding pair of pixelsrepresenting the same region of a physical feature based on othercharacteristics of the pixel and/or the context of information fromother neighboring or nearby pixels. Such recognition in correspondencemay be measured by a similarity metric between the feature vectors ofthe corresponding pair of pixels from the stereo image pair. As anexample, a cosine similarity between feature vectors may be used as asimilarity metric. A greater the similarity, the more likely it is thattwo pixels represented by the two feature vectors are corresponding tothe same physical real-life feature.

As mentioned above, a disparity value indicates the pixel correspondenceoffset between the stereo image pair for the same physical real-lifefeature. Given the path disparity model, a correct correspondence offsetin the one image of the stereo image pair can be computed for each pixelon the second image of the stereo image pair if it is a road pixel, butnot for a hazard pixel (a pixel corresponding to a hazard object). Ahazard on the path may be revealed by a hazard pixel, which would be apixel having a larger disparity than the disparity computed from thepath disparity model. A lack of predictable correspondence thusindicates that a pixel is not a component of the road and thereforpossibly represents a hazard present on the surface of the road, or adeformation in the road surface (e.g., pothole, cavity, etc.). In someembodiments, therefore, a path disparity map is generated from the pathdisparity model that can be used to provide path disparity for eachpixel of one image of the stereo image pair to find its respectivecorrespondence in the other image of the stereo image pair.

In embodiments, pixel correspondence information from disparity may befurther combined with the feature vectors computed by the DNN model tohelp distinguish hazard pixels from road pixels. For example, in oneembodiment, a first feature vector (FV1) is extracted for a pixel (P) ofa first image of the stereo image pair. A second feature vector (FV2) isextracted for a corresponding pixel of a second image of the stereoimage pair, where the corresponding pixel in the second image isdetermined by applying the path disparity model to the pixel (P) of thefirst image. A similarity between FV1 and FV2 can then be computed. Asdiscussed above, the path disparity model represents an accurate modelto predict correspondence based on the disparity of the path.Accordingly, substantial similarity is expected between FV1 and FV2 ifthe pixel (P) is a road pixel, and substantial dissimilarity is expectedif pixel (P) is a hazard pixel. Therefore, the similarity characteristicof corresponding feature vectors obtained using the path disparity modelcan further facilitate distinguishing hazard pixels from road pixelsduring classification. Determination of such similarities between FV1and FV2 can act as a second part of classification information includedwith the two associated feature vectors in addition to the first partdiscussed above. In some embodiments, similarity can be computed andrepresented by a vector similarity metric that is input to aclassifier—such as a machine learning classifier—along with the featureinformation extracted from the stereo image pair, to differentiatehazard pixels from non-hazard pixels.

The techniques disclosed herein provide an ego-machine with effectiveand robust hazard detection because the path disparity model isleveraged to compute shifts in pixel location between stereo image pairsand to use that pixel offset to augment information extracted from thestereo image pairs.

Various techniques may then be used to determine whether or not a pixelis classified as a hazard pixel (corresponding to a hazard). Forexample, a pixel classifier may use simple binary classification ofeither “hazard” or “non-hazard” based on a magnitude of the vectorsimilarity metric or other quantitative value computed from the featurevector similarity. For example, in some embodiments when a magnitude ofthe vector similarity metric is less than a similarity threshold, thepixel may be classified as a hazard. When the magnitude of the vectorsimilarity metric is greater than or equal to the similarity threshold,the pixel may be classified as a non-hazard. Moreover, because themagnitude of the similarity may be, at least in part, a function ofdistance of the object from the sensors, the similarity threshold mayvary as a function of image row. The similarity threshold, for example,may be computed as a rectified linear function of the image row.Non-limiting examples of such pixel classifier that may be used toimplement simple binary classification include a support vector machine(SVM) or a shallow neural network, which can be trained to distinguishhazard pixels from road pixels based on the two feature vectors that areextracted and associated by the path disparity model.

Alternatively, the pixel classifier may comprise a more robust machinelearning model/neural network, such as a Deep Neural Network (DNN), forexample, to discern hazard pixels from non-hazard pixels. In that case,the output of the pixel classifier may be an indication of confidencefor each pixel as to whether it is a hazard pixel or road pixel. Thefirst and second feature vectors are combined and input applied to thepixel classifier machine learning model. This combined input is providedfor each pixel comprised within the region of interest of the originalstereo image pair. Therefore, in addition to the semantic informationconveyed by each individual two feature vector (FV1 and FV2), thesimilarity information between the two associated feature vectors (FV1and FV2) is now further included to help distinguish hazard pixels fromroad ones in the classification process.

In some embodiments, the classification of pixels as hazards ornon-hazards is performed by the machine learning model that extracts thefirst and second feature vectors from the stereo image pair providingfor an end-to-end neural network/machine learning model that inputs thestereo image pair and outputs a classification for one or more pixels(e.g., each pixel) of the stereo image pair, or at least for thosepixels within the region of interest. In some embodiments, the pixelclassifier applies this pixel classification on a block-by-block basisto each of the individual blocks of pixels of the disparity map, andthen outputs a hazard map for each respective block. Alternatively, theoutput of the pixel classifier may comprise a composite image spacehazard map corresponding to the region of interest.

One advantage of using an end-to-end machine learning model is that itcan be trained using end-to-end training so that a common ground truthtraining data set can be used, and so that the feature extraction andpixel classification models are each trained to have a sharedunderstanding of semantic characteristics of hazard pixels versus roadpixels. In some embodiments, the one or more machine learning models aretrained using ground truth data that comprises consolidatedclassification labels corresponding to a drivable region, a non-drivableregion, and hazards. For example, classification labels for defining thenon-drivable region may be based at least in part by one or moreobstacle classes, and classification labels for the drivable region maybe defined at least in part by a freespace class.

In some embodiments, the ground truth data is optimized for training amachine learning model to discern hazards from non-hazards in thecontext of a region of interest comprising a path for an ego-machine.That is, it is not so important that the machine learning model be ableto classify what type of object a hazard pixel represent. Rather it issufficient that the machine learning model is able to accuratelydetermine that a given pixel is not a road pixel and from that inferthat it is a hazard without regard to what type of hazard. That is, anypixel corresponding to a foreign object on the road surface rather thanthe road surface, is a candidate for being a hazard pixel. Accordingly,given a premise that the ego-machine will travel on a designated pathsurface and avoid travel off of that designate path surface, and aregion of interest constrained to portions of that path surface, theground truth training data may omit labeling of individual objectsoutside of the region of interest as they are irrelevant.

In some embodiments, an automatic labelling process may be implementedto generate optimized ground truth data to facilitate the segmentationof hazards from the stereo image pair, both with respect to computingfeature vectors and pixel classification. In one embodiment, theautomatic labeling process inputs an initial set of training images anduses a DNN model to apply obstacle classes (e.g. cars and pedestrians)to assign non-drivable region labels. For example the DNN model maycomprise a combination of a semantic segmentation neural network modeland 2D object detection neural network model, and intersect them toincrease non-drivable region labeling accuracy. To generate the labelsfor drivable regions, the training DNN may comprise a drivable regionprediction model from the semantic segmentation neural networks model.As already mentioned herein, the prediction region can be restricted tothe region of interest (ROI), which can be determined from a freespaceneural networks model. As such, the initial training data can beautomatically labeled to distinguish regions as belonging to regionsthat are either: 1) drivable regions (determine from the intersection ofthe semantic segmentation of a drivable region, and the region ofinterest); 2) non-drivable regions (determined from the intersection ofsemantic segmentation of cars and pedestrian, two-dimensional boundingboxes of cars and pedestrians, and the region of interest); or 3) other(e.g., do not care, or unimportant) regions (e.g., all other regions notincluded in 1) and 2)). In some embodiments, the labeling of anon-drivable region (which within the ROI would equate to a hazard) maybe based on just a subset of that region. For example, using cars andpedestrians as the label of a non-drivable region, the DNN model mayover-fit to such objects and only detect them as hazards by using thesemantic meaning of such objects. As a result, the model may notrecognize the real hazard objects on the road. To disallow the DNN modelto utilize such semantic meaning of cars and pedestrian, someembodiments may limit the receptive field of the DNN model and preventit from seeing the whole object, but instead only part of it (such astires or a taillight in the case of a car, for example). The receptivefield of the extracted feature map may thus be restricted to areas nolarger than a predetermined size so that the features do not contain thesemantic meaning of any specific real world object. The predeterminedsize may be smaller than the size of a car, for example, so that thefeatures do not contain the semantic meaning of cars. In someembodiments, to make such a restriction, size of the convolution kernelis limited in the convolutional neural networks and the number ofconvolutional layers is limited, so that very high level features arenot extracted. Smaller receptive fields may also be used for the upperpart of the image, which represents smaller objects in the far awayregions.

It should be appreciated that when a pixel is classified by the pixelclassifier as a hazard pixel, and that pixel is an isolated hazard pixelnot adjacent to, or nearby, other hazard pixels, it is likely a falsepositive resulting from system noise. This is particularly the case fora lone isolated hazard pixel present in a block or image rowcorresponding to a point on the road near the ego-machine. Noise pixelstend to distribute randomly and sparsely in the image. When many hazardpixels are more densely clustered together, in contrast, they are muchless likely false positives. Therefore, in embodiments, a clusteringalgorithm is applied to those pixels, for example in image space, toremove false positive and define clusters of hazard pixels thatcorrelate to real hazard objects on the path of the ego-machine. Anon-limiting example of a type of clustering algorithm is theDensity-Based Spatial Clustering of Applications with Noise (DBSCAN)algorithm. DBSCAN groups together points that are closely packedtogether, removing outlier points in low-density regions with few nearbyneighbors. After clustering, an image space bounding shape may begenerated around the location of one or more clusters, identifyingobjects within those bounding shapes as hazards to be avoided by theego-machine. The bounding shapes may further be correlated back to theoriginal images captured by the sensors, to overlay indications of thedetected hazards onto the images, or generate other hazard detectionwarnings or signals. The hazard detection system may then definelocations of the one or more hazards using the clusters of pixels. Insome embodiments, a relative position and distance of the detectedhazards to the ego-machine may be computed.

The pair of image sensors (O, O′) may comprise two or moresynchronization cameras mounted on the autonomous vehicles withoverlapping Field of View (FOV) regions that include the forward path oftravel of the ego-machine. In some embodiments, both cameras share thesame optical specifications, such as focal length, and are synchronizedand pre-calibrated with their relative pose. In other embodiments, thepair of image sensors (O, O′) are synchronized, but have mismatchedspecifications. For example, the sensors may comprise a stereo camera,and/or combinations of a monocular camera, a surround camera, a fisheyecamera, or a wide view camera.

The path travelled by the ego-machine is not limited to any one type ofpath or surface and may include paths such as a paced road, an unpavedroad, a highway, a driveway, a portion of a parking lot, a trail, atrack, a walking path, a delineated portion of an environment, or anaircraft runway or landing pad, for example. It should also beunderstood that the path travelled by the ego-machine may be inside abuilding or other facility and comprise a hallway, corridor or isle, forexample.

The output generated by the hazard detection system may include a hazarddetection signal used to display a location of the detected hazard to anoperator of the ego-machine, or otherwise used by one or more downstreamcomponents of the ego-machine—such as a world model manager, a pathplanner, a control component, a localization component, an obstacleavoidance component, an actuation component, and/or the like—to performone or more operations for controlling the ego-machine through anenvironment. In some embodiments, communication between the hazarddetection system and such downstream components of the ego-machine isimplemented via an application programing interface (API).

For semi-autonomous ego-machines (or ego-machine operating in asemi-autonomous mode), information about detected hazards can bedisplayed to the ego-machine operator (e.g., on a heads-up windshielddisplay or other display screen) to assist the operator in deciding howto maneuver the ego-machine to avoid the hazard. Such a display caninclude a bounding box overlay or other warning graphic superimposed onan image of the path. In other embodiments where the ego-machine has ahigher degree of autonomy, the output generated by the hazard detectionsystem comprises a set of data stored to a memory or otherwisetransmitted to another ego-machine system that implements hazardavoidance functions. In some embodiments, information about detectedhazards, such as their relative position to the ego-machine, may bemaintained in memory for a predefined duration or, as a non-limitingexample, for as long as the hazard remains within a threshold distancefrom the ego-machine.

The hazard detection system and corresponding methods may be executed atleast in part on one or more processing units coupled to a memory. Theprocessing unit(s) are programmed to execute code to implement one ormore of the features and functions of the hazard detection system tocompute disparity maps, road (or path) disparity models, classify andcluster pixels, and other functions described herein. While in someembodiments, all processing is performed onboard the ego-machine, inother embodiments, features and functions of the hazard detection systemmay be distributed and performed by a combination of onboard processorsand cloud computing resources, and sensor data obtained from onboardsensors augmented with supplemental data obtained from a data center orother server. In such implementations, the ego-machine further comprisesat least one wireless communication interface for coupling the hazarddetection system to a wireless communications network.

With reference to FIG. 1 , FIG. 1 is an example data flow diagramillustrating the interconnection of components and flow of informationor data for machine learning based hazard detection using stereodisparity for an ego-machine (such as autonomous vehicle 900 discussedbelow with respect to FIG. 9A), in accordance with some embodiments ofthe present disclosure. It should be understood that this and otherarrangements described herein are set forth only as examples. Otherarrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. In some embodiments, the systems,methods, and processes described herein may be executed using similarcomponents, features, and/or functionality to those of exampleautonomous vehicle 900 of FIGS. 9A-9D, example computing device 1000 ofFIG. 10 , and/or example data center 1100 of FIG. 11 .

As shown in FIG. 1 , the hazard detection system executing the process100 includes a stereo disparity machine learning hazard detector 102that receives, as input, stereo sensor data 108 captured by a sensorpair 110. The stereo disparity machine learning hazard detector 102processes the stereo sensor data 108 to ascertain a path disparity model104 from one or more disparity maps 106. The stereo disparity machinelearning hazard detector 102 processes the stereo sensor data 108 withone or more neural network models 105 to compute pairs of featurevectors for features extracted from the stereo sensor data 108. Asfurther explained herein, stereo disparity machine learning hazarddetector 102 utilizes the combination of feature vectors and the pathdisparity model 104 to detect hazards on the surface of the path, orcaused by defects in the path itself. In this example, the stereo sensordata 108 may be derived from one or more on-board sensor pairs 110 of anego-machine (e.g., ego-machine 900 of FIGS. 9A-9D).

For example, with reference to FIG. 2 , FIG. 2 illustrates anoverlapping field of view (FOV) for a pair of cameras of vehicle 900, inaccordance with some embodiments of the present disclosure. FIG. 2includes vehicle 900, camera 202, camera 204 (which together form thesensor pair 110), field of view (FOV) 206, field of view (FOV) 208, andoverlapping field of view (FOV) region 210. For example, cameras 202 and204 may be synchronized stereo cameras that may be mounted on thevehicle 900—such as on a windshield or other area of the vehicle 900.The two cameras 202 and 204 may execute a cross-camera optical flow (OF)tracking algorithm to extract stereo disparity information from pairwiseimages. The camera 202 may provide the FOV 206 to the system and thecamera 204 may provide the FOV 208 to the system. In some embodiments,cross-camera OF tracking may be executed at least in the overlapping FOVregion 210 of the cameras 202 and 204. The overlapping FOV region 210covers the defined region of interest (ROI) that includes the path ofthe ego-machine. Based on a disparity between pixels in the camera imagedata in the overlapping FOV region 210 for a particular location, thesystem may determine both the path disparity model 104 and the disparitymaps 106.

The output from the stereo disparity machine learning hazard detector102 may include a hazard detection output indicative of hazard on thepath of the ego-machine. The hazard detection output may further includea location of the detected hazard, such as a relative position of thehazard with respect to the ego-machine. This hazard detection output maybe used by one or more downstream navigation components 124 of theego-machine such as the controller(s) 936 discussed below. Thedownstream navigation components 124, for example, may implement hazardavoidance navigation functions and/or world model manager, a pathplanner, a control component, a localization component, an obstacleavoidance component, an actuation component, and/or the like, to performoperations for controlling the ego-machine through an environment. Thehazard detection output may also, or instead, be input by ahuman-machine interface (HMI) 120 comprising a display (e.g., on aheads-up windshield display or other display screen) to the operator ofthe ego-machine. In embodiments, the relative position of detectedhazards can be displayed to the HMI display 120 to assist the operatorin deciding how to navigate the ego-machine.

In some embodiments, the stereo disparity machine learning hazarddetector 102 may also receive location information from navigationreceiver(s) 116 and/or may be coupled to a wireless network interface118. The navigation receiver(s) 114 may comprise, as non-limitingexamples, a GNSS receiver (e.g., a GPS receiver), satellite navigationsystem, inertial and/or dead reckoning, or other navigation system. Thewireless network interface 924 may be capable of communication over anyair interface protocol, such as but not limited to WiFi, 4G LTE, 5G NR,WCDMA, UMTS, GSM, CDMA2000, etc. In some embodiments, the stereodisparity machine learning hazard detector 102 may input locationinformation from the navigation receiver(s) 116 in order to track amotion of the ego-machine with respect to a relative position of adetected object on the path of the ego-machine. For example, in such anembodiment, may keep track of a relative position of a hazard object inmemory until the hazard object is no longer within a threshold distancefrom the ego-machine. In other embodiments, the hazard detectioninformation (for example, hazard size and/or location) may betransmitted via the wireless network interface 118 to a networked hazardtracking server and/or transmitted to another ego-machine system thatimplements hazard avoidance functions.

Now referring to FIG. 3 , FIG. 3 is a block diagram illustrating anexample stereo disparity machine learning hazard detector 102 that maybe implemented in at least one embodiment. As shown in FIG. 3 , thestereo disparity machine learning hazard detector 102 may comprise anROI filter 310, a blockwise image divider 312, a disparity mappingfunction 314, a path disparity modeling function 316, a machine learningfeature vector evaluator 318, a hazard element cluster function 320,and/or a hazard location memory 322. These various functions of thestereo disparity machine learning hazard detector 102 may be carried outby a processor executing instructions stored in memory and/or executedusing similar components, features, and/or functionality to those ofexample autonomous vehicle 900 of FIGS. 9A-9D, example computing device1000 of FIG. 10 , and/or example data center 1100 of FIG. 11 .

The stereo disparity machine learning hazard detector 102 may input thestereo sensor data 108 to compute the path disparity model and featurevectors, as described herein.

In embodiments, an ROI filter 310 may receive the stereo sensor data 108from the sensor pair 110 and perform an analysis in order to constrainprocessing to a defined ROI for hazard detection that includes the pathof the ego-machine from the overlapping FOV region 210. In someembodiments, the ROI for hazard detection and can be extracted from thestereo sensor data 108 using freespace detection, lane detection, orother known techniques. For example, one or more computer visionalgorithms, machine learning models, neural networks, and/or the likemay be trained or programmed to identify drivable freespace, lanes, roadboundaries, etc. Constraining hazard detection related processing to theROI avoids waste of computational resources on non-relevant regions ofthe captured images, thereby leading to more accurate hazard detection,reduced noise, and reduced runtime.

The disparity mapping function 314 inputs the pixels of the ROIextracted by the ROI filter 310 and computes an image space disparitymap for the ROI. As previously explained, an object point X observed bythe cameras 202 and 204 will have a pixel location at (x, y) in theimage captured by camera 202, and pixel location (x′, y′) in the imagecapture by camera 204. The stereo image pair represented by the stereosensor data 108 may be rectified by the disparity mapping function 314to where the image rows in the stereo image pair are aligned such thaty=y′. The disparity, d, for that pixel is then defined by the pixellocation difference: d=x−x′, and is proportional to sensor focal length,f, and the baseline length, b, between the two sensors 202 and 204, andis inversely proportional to the object depth, Z. The relationshipbetween these parameters can be expressed as: d=b*f/Z.

FIG. 4 illustrates an example 400 of a discontinuity in disparity valuesfor detecting a roadway hazard, in accordance with some embodiments ofthe present disclosure. FIG. 4 includes sensor pair 110, pixel 404,pixel 406, height 408, path 410 (e.g., a roadway), and an object 412. Ingeneral, the height of object 412 may cause an occlusion of the path 410behind the object 412 from a perspective of the sensor pair 110. Assuch, when the object 412 is present, there may be a discontinuity indisparity values indicative of a distance jump between the pixel 404corresponding to a top of the object 412 and the pixel 406 immediatelyabove the pixel 404—e.g., because the distance from the camera 402 tothe pixel 404 is different from the distance to the pixel 406, and thecamera 402 may not be able to accurately capture the portions of theroadway 410 occluded by the object 412.

Several known algorithms may be used for computing disparity values. Inone embodiment, Semi-Global Matching (SGM) may be applied to the ROI tocompute a globally consistent image space disparity map. With the SGMalgorithm, disparity for each individual pixel is computed based on amatching cost (such as a normalized cross correlation, for example), anda global optimization performed on those computed disparity valued baseon a disparity smoothness parameter. As such, disparity mapping function314 constructs an image space disparity map for the image pair isconstructed in which each element (e.g., pixel) of the disparity maprepresents a disparity for a corresponding element of the image of theROI.

The blockwise image divider 312 functions to subdivide the ROI and imagespace disparity map into a plurality of blocks. Each of these blocks maythen be treated as an individual processing unit for hazard detection.By subdividing the image space disparity map and performing hazarddetection on a block-by-block basis, the disparity of the road surfaceitself within each block is considerably less than if the entirety ofthe image space disparity map was directly evaluated. In someembodiments, block dimensions may vary as a function of the image rowsthey comprise. Blocks capturing parts of the road or path that arenearer the ego-machine or the sensors thereof may comprise more imagerows than those capturing parts of the road that are farther from theego-machine. The number of image rows within a block may be determinedbased on several factors, for example, by detecting a vanishing pointwhere the viewable segment of the road ends, the mounting position ofthe sensors, and/or having blocks approximately capture equivalent roadsurface areas.

While it is possible to detect hazards directly from the image spacediversity map, it may contain considerable errors due to a variety ofnoise sources during the generation process, including sensorcalibration and road and/or hazard textures. The path disparity modelingfunction 316 addresses these errors by computing a pattern of disparityfor the path traveled by the ego-machine, and from that builds amathematical path disparity model. For example, in embodiments, for eachof the plurality of blocks that the blockwise image divider 312 definesfrom the image space disparity map, the path disparity modeling function316 applies a linear model, such as map: d=f (r,c)=f(r), where d is thedisparity, and f is a function with image row(r), image column (c) asvariables. More specifically, the image space disparity map appearingwithin each of the blocks is updated by remapping image space disparityto V-disparity space. The result is a V-disparity map for eachrespective block. The computed disparity values of the road surface asit appears in the V-disparity map for each block becomes a smooth line,or even a straight line if the road surface is flat and parallel to theimage row. In one embodiment, the Random Sample Consensus (RANSAC) linefitting algorithm is applied to the V-disparity map for each respectiveblock to find a straight line model of the road disparity within eachblock.

Given the computed path disparity models, a path disparity map may begenerated by the path disparity modeling function 316 that can be usedto provide path disparity for each pixel of one image of the stereoimage pair to find its respective correspondence in the other image ofthe stereo image pair. Vector similarity metrics may then be computed toaugment semantic (e.g. texture and pattern) classification information.

In some embodiments, the machine learning feature vector evaluator 318comprises a feature vector processing function 330 and an elementclassification function 332, each of which may be implemented at leastin part by a DNN model. The feature vector processing function 330inputs the stereo image pair (e.g. from the stereo image data 108) toextract a feature map of the first image (for example, the left image),and extract a feature map of the second image (for example, the rightimage). A first set of feature vectors is computed for featuresextracted from the first image of the stereo image pair, and a secondset of features is computed for features extracted from the second imageof the stereo image pair. Pixels that represent hazards appearing in thestereo image pair are expected have a distinguishable texture andpattern characteristics as compared to pixels representing the surfaceof the path. These distinguishing characteristics can be captured anddescribed by the feature vectors computed by the DNN model implementingthe feature vector processing function 330, which may be trained torecognize such semantic information and texture information. Forexample, for a given pixel (P) of the first image of the stereo imagepair, a corresponding pixel of the second image of the stereo image pairis determined based on the path disparity model and/or path disparitymap. A first feature vector (FV1) is extracted for the pixel (P) of thefirst image and a second feature vector (FV2) is extracted for the pixeldetermined to be the corresponding pixel to pixel (P) in the secondimage. The two feature vectors FV1 and FV2 are thus associated featurevectors in that they are associated by the path disparity model. Thefeature vector processing function 330 determines the similarity betweenFV1 and FV2, which may be computed to produce a vector similaritymetric. In some embodiments, this vector similarity metric may becomputed for each pixel of the first image of the stereo image pair. Forany given pixel, a substantial similarity is expected between FV1 andthe corresponding FV2 of the pixel is a road pixel, and substantialdis-similarity is expected if pixel is a hazard pixel. The vectorsimilarity metric may thus be included with the semantic classificationinformation of the FV1 and FV2 feature vectors and provided by thefeature vector processing function 330 to the element classificationfunction 332 to differentiate hazard pixels from non-hazard pixels. Insome embodiments, the output of the feature vector processing function330 to the element classification function 332 comprises a featurevector map where for each pixel, the feature vector map includes the FV1and corresponding FV2 feature vectors including both the semanticclassification information and vector similarity information generatedby the feature vector processing function 330.

In some embodiments, the element classification function 332 processesthe FV1 and corresponding FV2 feature vectors (including both thesemantic classification information and vector similarity information)for each pixel and when a magnitude of the vector similarity metric at apixel is less than a similarity threshold, the element classificationfunction 332 classifies that pixel as a hazard pixel. The similaritythreshold may differ for different pixels of the image because theamount of similarity expected will vary as a function of distance of theobject from the sensors. Non-limiting examples of such pixel classifierthat may be used to implement simple binary classification include asupport vector machine (SVM) or a shallow neural network, which can betrained to distinguish hazard pixels from road pixels based on the twofeature vectors that are extracted and associated by the path disparitymodel.

In other embodiments, element classification function 332 may comprise amachine learning model/neural network, such as DNN model for example, todiscern hazard pixels from non-hazard pixels. The output of the elementclassification function 332 in that case may comprise an indication ofconfidence for each pixel as to whether it is a hazard pixel or roadpixel. The combined input of semantic classification information andvector similarity information for each pixel comprised within the regionof interest of the original stereo image pair is used by the elementclassification function 332 to classify each pixel as a hazard ornon-hazard element of the image. In some embodiments, the elementclassification function 332 applies this pixel classification on ablock-by-block basis to each of the individual blocks of pixels of thedisparity map, and then outputs a hazard map for each respective block.Alternatively, the output of the element classification function 332 maycomprise a composite image space hazard map corresponding to the regionof interest.

In some embodiments, the machine learning feature vector evaluator 318comprises and end-to-end neural network/machine learning model thatimplements one or more the functions of both the feature vectorprocessing function 330 and element classification function 332discussed herein.

The hazard element clustering 320 comprises a function (e.g. aclustering algorithm) applied in image space using the classificationresults computed by the machine learning feature vector evaluator 318,to remove false positive hazard element classifications and defineclusters of hazard elements that correlate to real hazard objects on thepath of the ego-machine. For example, an isolated hazard pixel, absentother adjacent or nearby hazard pixels, is likely a false positivehazard element classification resulting from system noise. This isparticularly the case for an isolated hazard pixel appearing at a pointon the path near the ego-machine, where a true hazard object wouldappear to grow larger as the ego-machine moves closer, and thus beexpected to appear in several pixels. Noise pixels tend to distributerandomly and sparsely in the image. When many hazard pixels are moredensely clustered together, in contrast, they are much likely lesslikely false positives, and more likely to be a true hazard object.

An example cluster algorithm that may be applied by the hazard elementclustering function 320 is the Density-Based Spatial Clustering ofApplications with Noise (DBSCAN) algorithm. In one embodiment, usingDBSCAN or an equivalent cluster algorithm, the hazard element clustering320 groups together elements classified as hazard elements, that arepacked together in close proximity in image space, removing elementsclassified as hazard elements that are otherwise outlier points inlow-density regions with few nearby neighbors.

After clustering, the stereo disparity machine learning hazard detector102 may generate the hazard detection output, for use by one or moredownstream navigation components 124 and/or HMI display 120 for example.In some embodiments, the hazard detection output a bounding shapegenerated by the stereo disparity machine learning hazard detector 102around the location of one or more clusters defined by the hazardelement clustering 320 function. The bounding shape assists inidentifying objects appearing on the path of the ego-machine as hazardsto be avoided by the ego-machine. The bounding shapes may be correlatedback to the original images captured by the sensor pair 110, or otherimages captured by other sensors of the ego-machine, to overlayindications of the detected hazards onto the images. The hazarddetection output may also trigger hazard detection warnings or othersignals.

In some embodiments, a relative position and distance of the detectedhazards to the ego-machine may be computed and stored as a hazardlocation in memory 322. In this way, the ego-machine can continue totrack the location of nearby detected hazard objects that may no longerappear in current stereo sensor data 108. For example, in someembodiments the stereo disparity machine learning hazard detector 102may track the movement of the ego-machine based on location informationreceived from navigation receiver(s) 116, and from the tracked movementupdate the relative position and/or distance of detected hazard objectsin the memory 322. In some embodiments, the stereo disparity machinelearning hazard detector 102 may keep track of the relative position ofone or more detected hazard objects in hazard location in memory 322 foras long as they are within a threshold distance from the ego-machine,and optionally purge them from the memory 322 once they are further awaythan the threshold distance.

FIG. 5 is a flow diagram showing a method 500 for machine learning basedhazard detection using stereo disparity, in accordance with someembodiments of the present disclosure. Each block of method 500,described herein, comprises a computing process that may be performedusing any combination of hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory. The method 500 may also be embodied ascomputer-usable instructions stored on computer storage media. Themethod 500 may be provided by a standalone application, a service orhosted service (standalone or in combination with another hostedservice), or a plug-in to another product, to name a few.

In addition, method 500 is described, by way of example, with respect tothe hazard detection system included in the process 100 of FIG. 1 .However, this method 500 may additionally or alternatively be executedby any one system, or any combination of systems, including, but notlimited to, those described herein. It should therefore be understoodthat the features and elements described herein with respect to themethod 500 of FIG. 5 may be used in conjunction with, in combinationwith, or substituted for elements of, any of the other embodimentsdiscussed herein and vice versa. Further, it should be understood thatthe functions, structures, and other descriptions of elements forembodiments described in FIG. 5 may apply to like or similarly named ordescribed elements across any of the figures and/or embodimentsdescribed herein and vice versa.

The method 500 is drawn to detecting real-world hazardous objects in thepath of an ego-machine based on extracted feature vectors and a pathdisparity model. Generally, the method 500 comprises determining alocation of one or more pixels corresponding to one or more hazardobjects on a path of an ego-machine based at least in part onclassifying the one or more pixels using a first feature vectorcorresponding to a first image and a second feature vector correspondingto a second image, the first feature vector and the second featurevector determined to correspond to one another based at least in part ona path disparity model computed using a disparity map generated usingthe first image and the second image.

The method 500 begins at B510 with generating a disparity map indicativeof disparities between a first image generated using a first sensor anda second image generated using a second sensor. The first sensor and thesecond sensor have at least partially overlapping fields of viewincluding at least a portion of a path of an ego-machine. In someembodiments, the method includes receiving a stereo image pair for aregion of interest in a path of an ego-machine. The first and secondsensors produce the stereo image pair. For example, FIG. 6 at 610illustrates an image 612 from one of the stereo image pair showing thepath 614 traveled by the ego-machine, and a delineated ROI 616 withinwhich hazard detection is performed. At 620, an image space disparitymap 622 is shown for the delineated ROI 616. Each pixel of the imagespace disparity map 622 within the ROI 616 represents the computeddisparity for that pixel between the left and right images of the stereoimage pair. As indicated by the image space disparity map 622, disparityof the surface of the path 614 inherently increases as points on thesurface draw closer to the ego-machine.

The method 500 at B512 includes generating a path disparity model basedat least in part on the disparity map. In some embodiments, to generatethe path disparity model, the method applies a blockwise division to theimage space disparity map. As shown in FIG. 6 at 620, the blockwisedivision subdivides the disparity map 622 into a plurality of blocks624, each comprising a smaller disparity map. Disparity of the roadsurface within each block 624 is considerably less than the disparity ofthe entire road surface as a whole, so that within each block 624disparity caused by hazards is more readily distinguishable. In someembodiments, applying blockwise division may be omitted for at leastpart of the ROI, for example where the path traveled by the ego-machineis particularly narrow, and/or or substantially flat and smooth (such aswhere the path is a narrow tiled hallway, for example).

In some embodiments, an updated disparity map comprising a V-spacedisparity map, which may be equivalently referred to herein as aV-disparity map. FIG. 6 at 630 illustrates a plurality of V-disparitymaps 632 each plotting the computed disparity in V-space for one of theblocks 624 of the image space disparity map 622 in which at least aportion of the ROI 616 is included. Each of the plurality of V-disparitymaps 632 includes a linear path disparity model 634, which may becomputed by performing a line fitting algorithm against the disparityvalues appearing within that respective block 624. The computeddisparity values of the road surface as it appears in the V-disparitymap for each block 624 becomes a smooth line, or even a straight linedepending on the degree to which the road surface is flat and parallelto the image row. The line representing the path disparity model 634approximates the best fit line through the V-space plotted disparityvalues within the block.

When blockwise division is applied, then method 500 at B512 may beperformed for each block 624. Else, the method 500 may be performed onthe undivided image space disparity map.

The method 500 proceeds to B514 with computing, using one or moremachine learning models, one or more first feature vectors correspondingto the first image and one or more second feature vectors correspondingto the second image. For example, a first feature vector (FV1) isextracted for a pixel (P) of a first image of the stereo image pair. Asecond feature vector (FV2) is extracted for a corresponding pixel of asecond image of the stereo image pair. The corresponding pixel in thesecond image is determined by applying the path disparity model to thepixel (P) of the first image. The two feature vectors FV1 and FV2 arethus associated feature vectors in that they are associated by the pathdisparity model.

The method 500 at B516 includes determining a similarity between a firstfeature vector of the one or more first feature vectors and a secondfeature vector of the one or more second feature vectors that isassociated with the first feature vector based at least in part on thepath disparity model. The method 500 determines the similarity betweenFV1 and FV2, which may be computed and expressed as a vector similaritymetric. In some embodiments, this vector similarity metric may becomputed for each pixel of the first image of the stereo image pair. Forany given pixel, a substantial similarity is expected between FV1 andthe corresponding FV2 if the pixel is a road pixel, and substantialdis-similarity is expected if pixel is a hazard pixel. The vectorsimilarity metric may be incorporated into the FV1 and FV2 along withthe semantic classification information extracted from the stereo imagepair.

The method 500 at B518 includes classifying one or more pixels of atleast one of the first image or the second image as corresponding to oneor more hazards based at least in part on the first feature vector andthe second feature vector. The method processes a respective firstfeature vector FV1 and its corresponding FV2 feature vector for each ofthe one or more pixels, including both the semantic classificationinformation and vector similarity information. In some embodiments, whena magnitude of the vector similarity metric at a pixel is less than asimilarity threshold, the method classifies that pixel as a hazardpixel. When the magnitude of the vector similarity metric at a pixel isgreater than the similarity threshold, the method classifies that pixelas a non-hazard pixel (e.g., a road pixel). The similarity threshold maydiffer for different pixels of the image because the amount ofsimilarity expected will vary as a function of distance of the objectfrom the sensors.

In some embodiments, the method 500 at B518 comprises applying a machinelearning model/neural network, such as DNN model for example, to discernhazard pixels from non-hazard pixels. The method may output anindication of confidence for each pixel as to whether it is a hazardpixel or road pixel. The combined input of semantic classificationinformation and vector similarity information for each pixel comprisedwithin the region of interest of the original stereo image pair is usedby the DNN model to classify each pixel as a hazard or non-hazardelement of the image.

The method 500 may also include, as shown at B520, applying clusteringin image space to define the one or more hazards as hazard objects.Clustering is applied to remove false positive hazard elementclassifications and define clusters of hazard elements that correlate toreal hazard objects on the path of the ego-machine. In at least oneembodiment, a clustering algorithm is applied to elements classified bythe method as hazard elements to generate one or more clusters of hazardelements. Isolated hazard pixels sometimes represent false positivesresulting from system noise while true hazard pixels are more denselyclustered together. FIG. 7 illustrates example results of applyingclustering in image space to define hazard objects. An original imagecaptured by the sensor pair is shown at 710 and the results ofclustering shown at 712. Pixels that were classified as hazard pixels bythe method at 524, but that do not form clusters, are shown at 720.Pixels that do form a cluster are shown at 722. Such hazard elementclusters are considered to represent actual hazard objects on the pathof the ego-machine. In some embodiments, the method 500 may identify oneor more hazards as being represented by the sensor data based at leastin part on correlating the hazard element cluster 722 with the originalimage from sensor data. For example, the hazard element cluster 722 isan accurate detection of the location of an actual hazard object 732 (inthis example, a small dog) within the region of interest 734 within thepath 736 of the ego-machine. The method may further apply a clusteringalgorithm to the one or more pixels and the one or more other pixels togenerate clusters of pixels to define locations of the one or morehazards using the clusters of pixels. In some embodiments, thedefinition of the locations includes generating bounding shapes aroundeach respective cluster of pixels of the clusters of pixels.

The method 500 at B522 may further include generating a hazard detectionoutput. The hazard detection output may indicate a presence of thehazard on the path of the ego-machine. The hazard detection output mayinclude a location of the detected hazard, such as a relative positionof the hazard with respect to the ego-machine. This hazard detectionoutput may be used by one or more downstream navigation components toperform one or more automated operations based at least in part on thelocation of the detected hazards. The hazard detection output may alsobe sent to an HMI to provide a hazard warning or otherwise assist anoperator in deciding how to navigate the ego-machine. As such the method500 may include performing one or more operations for controlling theego-machine along the path based at least in part on the one or morehazards.

As previously mentioned, one advantage of using an end-to-end machinelearning model is that it can be trained using end-to-end training sothat a common ground truth training data set can be used. The imageextraction and pixel classification models are each trained to have ashared understanding of the semantic pattern and texturalcharacteristics of hazard pixels versus road pixels. As shown by themethod 800 in FIG. 8 , in some embodiments, the one or more machinelearning models of the machine learning feature vector evaluator 318 maybe trained using ground truth data that comprises consolidatedclassification labels corresponding to a drivable region, a non-drivableregion, and hazards. For example classification labels for defining thenon-drivable region may be based at least in part on one or moreobstacle classes, and classification labels for the drivable region maybe defined at least in part by a freespace class. In some embodiments,the ground truth data is optimized by method 800 for training a machinelearning model to discern hazards from non-hazards in the context of aregion of interest comprising a path for an ego-machine. By startingwith a premise that the ego-machine will travel on a designated pathsurface and avoid travel off of that designate path surface, and thatthe region of interest is constrained to portions of that path surface,the ground truth training data may omit labeling of individual objectsoutside of the region of interest as they are irrelevant.

Method 800 thus describes an automatic labelling process that may beimplemented to generate optimized ground truth data to facilitate thesegmentation of hazards from the stereo image pair. The process may beapplied with respect to computing feature vectors, determiningsimilarities between associated feature vectors, and performing pixelclassification. The method begins at B810 with receiving an initial setof training images. At B812, the method includes using a trained machinelearning model (such as a DNN model, for example) to apply obstacleclasses to assign non-drivable region labels. Such obstacle classes maycomprise, for example, cars and pedestrians. The trained machinelearning model may comprise an intersection of a semantic segmentationneural network model and a 2D object detection neural network model toincrease non-drivable region labeling accuracy. The method 800 at B814includes using the trained machine learning model to assign drivableregion labels for drivable regions. The trained machine learning modelmay comprise a drivable region prediction model from the semanticsegmentation neural networks model. The method 800 may includerestricting the prediction region to a region of interest (ROI), forwhich the ROI can be determined from a freespace neural networks model.The method at B816 includes labeling regions of the initial trainingimages as belonging to either drivable regions, non-drivable regions, ordo-not-care regions.

The labeling of a non-drivable region may be based on just a subset ofthat region. For example, using cars and pedestrians as the label of anon-drivable region, the machine learning model may overfit to suchobjects and only detect them as hazards by using the semantic meaning ofsuch objects. As a result, the model may not recognize the real hazardobjects on the road. To disallow the machine learning model to use suchsemantic meaning of cars and pedestrian, some embodiments may limit thereceptive field of the machine learning model in order to prevent themodel from seeing the whole object, but instead only part of it (such astires or a taillight in the case of a car, for example) as previouslydiscussed.

Example Autonomous Vehicle

FIG. 9A is an illustration of an example autonomous vehicle 900, inaccordance with some embodiments of the present disclosure. Theautonomous vehicle 900 (alternatively referred to herein as the “vehicle900”) may include, without limitation, a passenger vehicle, such as acar, a truck, a bus, a first responder vehicle, a shuttle, an electricor motorized bicycle, a motorcycle, a fire truck, a police vehicle, anambulance, a boat, a construction vehicle, an underwater craft, a drone,a vehicle coupled to a trailer, and/or another type of vehicle (e.g.,that is unmanned and/or that accommodates one or more passengers).Autonomous vehicles are generally described in terms of automationlevels, defined by the National Highway Traffic Safety Administration(NHTSA), a division of the US Department of Transportation, and theSociety of Automotive Engineers (SAE) “Taxonomy and Definitions forTerms Related to Driving Automation Systems for On-Road Motor Vehicles”(Standard No. J3016-201806, published on Jun. 15, 2018, Standard No.J3016-201609, published on Sep. 30, 2016, and previous and futureversions of this standard). The vehicle 900 may be capable offunctionality in accordance with one or more of Level 3-Level 5 of theautonomous driving levels. The vehicle 900 may be capable offunctionality in accordance with one or more of Level 1-Level 5 of theautonomous driving levels. For example, the vehicle 900 may be capableof driver assistance (Level 1), partial automation (Level 2),conditional automation (Level 3), high automation (Level 4), and/or fullautomation (Level 5), depending on the embodiment. The term“autonomous,” as used herein, may include any and/or all types ofautonomy for the vehicle 900 or other machine, such as being fullyautonomous, being highly autonomous, being conditionally autonomous,being partially autonomous, providing assistive autonomy, beingsemi-autonomous, being primarily autonomous, or other designation.

The vehicle 900 may include components such as a chassis, a vehiclebody, wheels (e.g., 2, 4, 6, 8, 18, etc.), tires, axles, and othercomponents of a vehicle. The vehicle 900 may include a propulsion system950, such as an internal combustion engine, hybrid electric power plant,an all-electric engine, and/or another propulsion system type. Thepropulsion system 950 may be connected to a drive train of the vehicle900, which may include a transmission, to enable the propulsion of thevehicle 900. The propulsion system 950 may be controlled in response toreceiving signals from the throttle/accelerator 952.

A steering system 954, which may include a steering wheel, may be usedto steer the vehicle 900 (e.g., along a desired path or route) when thepropulsion system 950 is operating (e.g., when the vehicle is inmotion). The steering system 954 may receive signals from a steeringactuator 956. The steering wheel may be optional for full automation(Level 5) functionality.

The brake sensor system 946 may be used to operate the vehicle brakes inresponse to receiving signals from the brake actuators 948 and/or brakesensors.

Controller(s) 936, which may include one or more system on chips (SoCs)904 (FIG. 9C) and/or GPU(s), may provide signals (e.g., representativeof commands) to one or more components and/or systems of the vehicle900. For example, the controller(s) may send signals to operate thevehicle brakes via one or more brake actuators 948, to operate thesteering system 954 via one or more steering actuators 956, to operatethe propulsion system 950 via one or more throttle/accelerators 952. Thecontroller(s) 936 may include one or more onboard (e.g., integrated)computing devices (e.g., supercomputers) that process sensor signals,and output operation commands (e.g., signals representing commands) toenable autonomous driving and/or to assist a human driver in driving thevehicle 900. The controller(s) 936 may include a first controller 936for autonomous driving functions, a second controller 936 for functionalsafety functions, a third controller 936 for artificial intelligencefunctionality (e.g., computer vision), a fourth controller 936 forinfotainment functionality, a fifth controller 936 for redundancy inemergency conditions, and/or other controllers. In some examples, asingle controller 936 may handle two or more of the abovefunctionalities, two or more controllers 936 may handle a singlefunctionality, and/or any combination thereof.

The controller(s) 936 may provide the signals for controlling one ormore components and/or systems of the vehicle 900 in response to sensordata received from one or more sensors (e.g., sensor inputs). The sensordata may be received from, for example and without limitation, globalnavigation satellite systems sensor(s) 958 (e.g., Global PositioningSystem sensor(s)), RADAR sensor(s) 960, ultrasonic sensor(s) 962, LIDARsensor(s) 964, inertial measurement unit (IMU) sensor(s) 966 (e.g.,accelerometer(s), gyroscope(s), magnetic compass(es), magnetometer(s),etc.), microphone(s) 996, stereo camera(s) 968, wide-view camera(s) 970(e.g., fisheye cameras), infrared camera(s) 972, surround camera(s) 974(e.g., 360 degree cameras), long-range and/or mid-range camera(s) 998,speed sensor(s) 944 (e.g., for measuring the speed of the vehicle 900),vibration sensor(s) 942, steering sensor(s) 940, brake sensor(s) (e.g.,as part of the brake sensor system 946), and/or other sensor types.Moreover, the controllers(s) 936 may receive the hazard detection outputfrom the stereo disparity machine learning hazard detector 102indicative of hazards on the path of the ego-machine and/or the relativeposition of hazards with respect to the ego-machine.

One or more of the controller(s) 936 may receive inputs (e.g.,represented by input data) from an instrument cluster 932 of the vehicle900 and provide outputs (e.g., represented by output data, display data,etc.) via a human-machine interface (HMI) display 934, an audibleannunciator, a loudspeaker, and/or via other components of the vehicle900. The outputs may include information such as vehicle velocity,speed, time, map data (e.g., the HD map 922 of FIG. 9C), location data(e.g., the vehicle's 900 location, such as on a map), direction,location of other vehicles (e.g., an occupancy grid), information aboutobjects and status of objects as perceived by the controller(s) 936,etc. For example, the HMI display 934 may display information about thepresence of one or more objects (e.g., a street sign, caution sign,traffic light changing, etc.), and/or information about drivingmaneuvers the vehicle has made, is making, or will make (e.g., changinglanes now, taking exit 34B in two miles, etc.). Moreover, the HMIdisplay 934 may display bounding shapes or other indications ofhazardous objects detected by the stereo disparity machine learninghazard detector 102.

The vehicle 900 further includes a network interface 924 which may useone or more wireless antenna(s) 926 and/or modem(s) to communicate overone or more networks. For example, the network interface 924 may becapable of communication over LTE, WCDMA, UMTS, GSM, CDMA2000, etc. Thewireless antenna(s) 926 may also enable communication between objects inthe environment (e.g., vehicles, mobile devices, etc.), using local areanetwork(s), such as Bluetooth, Bluetooth LE, Z-Wave, ZigBee, etc.,and/or low power wide-area network(s) (LPWANs), such as LoRaWAN, SigFox,etc. In some embodiments, the stereo disparity machine learning hazarddetector 102 may communicate detected hazardous objects to cloud basedservices or other ego-machines via the network interface 924.

FIG. 9B is an example of camera locations and fields of view for theexample autonomous vehicle 900 of FIG. 9A, in accordance with someembodiments of the present disclosure. The cameras and respective fieldsof view are one example embodiment and are not intended to be limiting.For example, additional and/or alternative cameras may be includedand/or the cameras may be located at different locations on the vehicle900. In various embodiments, sets of cameras as illustrated in FIG. 9Bmay be used as the sensor pair 110 to capture the stereo sensor data 108that is ultimately input to the stereo disparity machine learning hazarddetector 102.

The camera types for the cameras may include, but are not limited to,digital cameras that may be adapted for use with the components and/orsystems of the vehicle 900. The camera(s) may operate at automotivesafety integrity level (ASIL) B and/or at another ASIL. The camera typesmay be capable of any image capture rate, such as 60 frames per second(fps), 120 fps, 240 fps, etc., depending on the embodiment. The camerasmay be capable of using rolling shutters, global shutters, another typeof shutter, or a combination thereof. In some examples, the color filterarray may include a red clear clear clear (RCCC) color filter array, ared clear clear blue (RCCB) color filter array, a red blue green clear(RBGC) color filter array, a Foveon X3 color filter array, a Bayersensors (RGGB) color filter array, a monochrome sensor color filterarray, and/or another type of color filter array. In some embodiments,clear pixel cameras, such as cameras with an RCCC, an RCCB, and/or anRBGC color filter array, may be used in an effort to increase lightsensitivity.

In some examples, one or more of the camera(s) may be used to performadvanced driver assistance systems (ADAS) functions (e.g., as part of aredundant or fail-safe design). For example, a Multi-Function MonoCamera may be installed to provide functions including lane departurewarning, traffic sign assist and intelligent headlamp control. One ormore of the camera(s) (e.g., all of the cameras) may record and provideimage data (e.g., video) simultaneously.

One or more of the cameras may be mounted in a mounting assembly, suchas a custom designed (3-D printed) assembly, in order to cut out straylight and reflections from within the car (e.g., reflections from thedashboard reflected in the windshield mirrors) which may interfere withthe camera's image data capture abilities. With reference to wing-mirrormounting assemblies, the wing-mirror assemblies may be custom 3-Dprinted so that the camera mounting plate matches the shape of thewing-mirror. In some examples, the camera(s) may be integrated into thewing-mirror. For side-view cameras, the camera(s) may also be integratedwithin the four pillars at each corner of the cabin.

Cameras with a field of view that include portions of the environment infront of the vehicle 900 (e.g., front-facing cameras) may be used forsurround view, to help identify forward facing paths and obstacles, aswell aid in, with the help of one or more controllers 936 and/or controlSoCs, providing information critical to generating an occupancy gridand/or determining the preferred vehicle paths. Front-facing cameras maybe used to perform many of the same ADAS functions as LIDAR, includingemergency braking, pedestrian detection, and collision avoidance.Front-facing cameras may also be used for ADAS functions and systemsincluding Lane Departure Warnings (LDW), Autonomous Cruise Control(ACC), and/or other functions such as traffic sign recognition.

A variety of cameras may be used in a front-facing configuration,including, for example, a monocular camera platform that includes a CMOS(complementary metal oxide semiconductor) color imager. Another examplemay be a wide-view camera(s) 970 that may be used to perceive objectscoming into view from the periphery (e.g., pedestrians, crossing trafficor bicycles). Although only one wide-view camera is illustrated in FIG.9B, there may any number of wide-view cameras 970 on the vehicle 900. Inaddition, long-range camera(s) 998 (e.g., a long-view stereo camerapair) may be used for depth-based object detection, especially forobjects for which a neural network has not yet been trained. Thelong-range camera(s) 998 may also be used for object detection andclassification, as well as basic object tracking.

One or more stereo cameras 968 may also be included in a front-facingconfiguration. The stereo camera(s) 968 may include an integratedcontrol unit comprising a scalable processing unit, which may provide aprogrammable logic (FPGA) and a multi-core micro-processor with anintegrated CAN or Ethernet interface on a single chip. Such a unit maybe used to generate a 3-D map of the vehicle's environment, including adistance estimate for all the points in the image. An alternative stereocamera(s) 968 may include a compact stereo vision sensor(s) that mayinclude two camera lenses (one each on the left and right) and an imageprocessing chip that may measure the distance from the vehicle to thetarget object and use the generated information (e.g., metadata) toactivate the autonomous emergency braking and lane departure warningfunctions. Other types of stereo camera(s) 968 may be used in additionto, or alternatively from, those described herein.

Cameras with a field of view that include portions of the environment tothe side of the vehicle 900 (e.g., side-view cameras) may be used forsurround view, providing information used to create and update theoccupancy grid, as well as to generate side impact collision warnings.For example, surround camera(s) 974 (e.g., four surround cameras 974 asillustrated in FIG. 9B) may be positioned to on the vehicle 900. Thesurround camera(s) 974 may include wide-view camera(s) 970, fisheyecamera(s), 360 degree camera(s), and/or the like. For example, fourfisheye cameras may be positioned on the vehicle's front, rear, andsides. In an alternative arrangement, the vehicle may use three surroundcamera(s) 974 (e.g., left, right, and rear), and may leverage one ormore other camera(s) (e.g., a forward-facing camera) as a fourthsurround view camera.

Cameras with a field of view that include portions of the environment tothe rear of the vehicle 900 (e.g., rear-view cameras) may be used forpark assistance, surround view, rear collision warnings, and creatingand updating the occupancy grid. A wide variety of cameras may be usedincluding, but not limited to, cameras that are also suitable as afront-facing camera(s) (e.g., long-range and/or mid-range camera(s) 998,stereo camera(s) 968), infrared camera(s) 972, etc.), as describedherein.

FIG. 9C is a block diagram of an example system architecture for theexample autonomous vehicle 900 of FIG. 9A, in accordance with someembodiments of the present disclosure. It should be understood that thisand other arrangements described herein are set forth only as examples.Other arrangements and elements (e.g., machines, interfaces, functions,orders, groupings of functions, etc.) may be used in addition to orinstead of those shown, and some elements may be omitted altogether.Further, many of the elements described herein are functional entitiesthat may be implemented as discrete or distributed components or inconjunction with other components, and in any suitable combination andlocation. Various functions described herein as being performed byentities may be carried out by hardware, firmware, and/or software. Forinstance, various functions may be carried out by a processor executinginstructions stored in memory.

Each of the components, features, and systems of the vehicle 900 in FIG.9C are illustrated as being connected via bus 902. The bus 902 mayinclude a Controller Area Network (CAN) data interface (alternativelyreferred to herein as a “CAN bus”). A CAN may be a network inside thevehicle 900 used to aid in control of various features and functionalityof the vehicle 900, such as actuation of brakes, acceleration, braking,steering, windshield wipers, etc. A CAN bus may be configured to havedozens or even hundreds of nodes, each with its own unique identifier(e.g., a CAN ID). The CAN bus may be read to find steering wheel angle,ground speed, engine revolutions per minute (RPMs), button positions,and/or other vehicle status indicators. The CAN bus may be ASIL Bcompliant.

Although the bus 902 is described herein as being a CAN bus, this is notintended to be limiting. For example, in addition to, or alternativelyfrom, the CAN bus, FlexRay and/or Ethernet may be used. Additionally,although a single line is used to represent the bus 902, this is notintended to be limiting. For example, there may be any number of busses902, which may include one or more CAN busses, one or more FlexRaybusses, one or more Ethernet busses, and/or one or more other types ofbusses using a different protocol. In some examples, two or more busses902 may be used to perform different functions, and/or may be used forredundancy. For example, a first bus 902 may be used for collisionavoidance functionality and a second bus 902 may be used for actuationcontrol. In any example, each bus 902 may communicate with any of thecomponents of the vehicle 900, and two or more busses 902 maycommunicate with the same components. In some examples, each SoC 904,each controller 936, and/or each computer within the vehicle may haveaccess to the same input data (e.g., inputs from sensors of the vehicle900), and may be connected to a common bus, such the CAN bus.

The vehicle 900 may include one or more controller(s) 936, such as thosedescribed herein with respect to FIG. 9A. The controller(s) 936 may beused for a variety of functions. The controller(s) 936 may be coupled toany of the various other components and systems of the vehicle 900, andmay be used for control of the vehicle 900, artificial intelligence ofthe vehicle 900, infotainment for the vehicle 900, and/or the like. Forexample, features and function of the stereo disparity machine learninghazard detector 102 may be at least in part executed by the one or morecontroller(s) 936.

The vehicle 900 may include a system(s) on a chip (SoC) 904. The SoC 904may include CPU(s) 906, GPU(s) 908, processor(s) 910, cache(s) 912,accelerator(s) 914, data store(s) 916, and/or other components andfeatures not illustrated. The SoC(s) 904 may be used to control thevehicle 900 in a variety of platforms and systems. For example, theSoC(s) 904 may be combined in a system (e.g., the system of the vehicle900) with an HD map 922 which may obtain map refreshes and/or updatesvia a network interface 924 from one or more servers (e.g., server(s)978 of FIG. 9D).

The CPU(s) 906 may include a CPU cluster or CPU complex (alternativelyreferred to herein as a “CCPLEX”). The CPU(s) 906 may include multiplecores and/or L2 caches. For example, in some embodiments, the CPU(s) 906may include eight cores in a coherent multi-processor configuration. Insome embodiments, the CPU(s) 906 may include four dual-core clusterswhere each cluster has a dedicated L2 cache (e.g., a 2 MB L2 cache). TheCPU(s) 906 (e.g., the CCPLEX) may be configured to support simultaneouscluster operation enabling any combination of the clusters of the CPU(s)906 to be active at any given time.

The CPU(s) 906 may implement power management capabilities that includeone or more of the following features: individual hardware blocks may beclock-gated automatically when idle to save dynamic power; each coreclock may be gated when the core is not actively executing instructionsdue to execution of WFI/WFE instructions; each core may be independentlypower-gated; each core cluster may be independently clock-gated when allcores are clock-gated or power-gated; and/or each core cluster may beindependently power-gated when all cores are power-gated. The CPU(s) 906may further implement an enhanced algorithm for managing power states,where allowed power states and expected wakeup times are specified, andthe hardware/microcode determines the best power state to enter for thecore, cluster, and CCPLEX. The processing cores may support simplifiedpower state entry sequences in software with the work offloaded tomicrocode.

The GPU(s) 908 may include an integrated GPU (alternatively referred toherein as an “iGPU”). The GPU(s) 908 may be programmable and may beefficient for parallel workloads. The GPU(s) 908, in some examples, mayuse an enhanced tensor instruction set. The GPU(s) 908 may include oneor more streaming microprocessors, where each streaming microprocessormay include an L1 cache (e.g., an L1 cache with at least 96 KB storagecapacity), and two or more of the streaming microprocessors may share anL2 cache (e.g., an L2 cache with a 512 KB storage capacity). In someembodiments, the GPU(s) 908 may include at least eight streamingmicroprocessors. The GPU(s) 908 may use compute application programminginterface(s) (API(s)). In addition, the GPU(s) 908 may use one or moreparallel computing platforms and/or programming models (e.g., NVIDIA'sCUDA).

The GPU(s) 908 may be power-optimized for best performance in automotiveand embedded use cases. For example, the GPU(s) 908 may be fabricated ona Fin field-effect transistor (FinFET). However, this is not intended tobe limiting and the GPU(s) 908 may be fabricated using othersemiconductor manufacturing processes. Each streaming microprocessor mayincorporate a number of mixed-precision processing cores partitionedinto multiple blocks. For example, and without limitation, 64 PF32 coresand 32 PF64 cores may be partitioned into four processing blocks. Insuch an example, each processing block may be allocated 16 FP32 cores, 8FP64 cores, 16 INT32 cores, two mixed-precision NVIDIA TENSOR COREs fordeep learning matrix arithmetic, an L0 instruction cache, a warpscheduler, a dispatch unit, and/or a 64 KB register file. In addition,the streaming microprocessors may include independent parallel integerand floating-point data paths to provide for efficient execution ofworkloads with a mix of computation and addressing calculations. Thestreaming microprocessors may include independent thread schedulingcapability to enable finer-grain synchronization and cooperation betweenparallel threads. The streaming microprocessors may include a combinedL1 data cache and shared memory unit in order to improve performancewhile simplifying programming.

The GPU(s) 908 may include a high bandwidth memory (HBM) and/or a 16 GBHBM2 memory subsystem to provide, in some examples, about 900 GB/secondpeak memory bandwidth. In some examples, in addition to, oralternatively from, the HBM memory, a synchronous graphics random-accessmemory (SGRAM) may be used, such as a graphics double data rate typefive synchronous random-access memory (GDDR5).

The GPU(s) 908 may include unified memory technology including accesscounters to allow for more accurate migration of memory pages to theprocessor that accesses them most frequently, thereby improvingefficiency for memory ranges shared between processors. In someexamples, address translation services (ATS) support may be used toallow the GPU(s) 908 to access the CPU(s) 906 page tables directly. Insuch examples, when the GPU(s) 908 memory management unit (MMU)experiences a miss, an address translation request may be transmitted tothe CPU(s) 906. In response, the CPU(s) 906 may look in its page tablesfor the virtual-to-physical mapping for the address and transmits thetranslation back to the GPU(s) 908. As such, unified memory technologymay allow a single unified virtual address space for memory of both theCPU(s) 906 and the GPU(s) 908, thereby simplifying the GPU(s) 908programming and porting of applications to the GPU(s) 908.

In addition, the GPU(s) 908 may include an access counter that may keeptrack of the frequency of access of the GPU(s) 908 to memory of otherprocessors. The access counter may help ensure that memory pages aremoved to the physical memory of the processor that is accessing thepages most frequently.

The SoC(s) 904 may include any number of cache(s) 912, including thosedescribed herein. For example, the cache(s) 912 may include an L3 cachethat is available to both the CPU(s) 906 and the GPU(s) 908 (e.g., thatis connected both the CPU(s) 906 and the GPU(s) 908). The cache(s) 912may include a write-back cache that may keep track of states of lines,such as by using a cache coherence protocol (e.g., MEI, MESI, MSI,etc.). The L3 cache may include 4 MB or more, depending on theembodiment, although smaller cache sizes may be used.

The SoC(s) 904 may include an arithmetic logic unit(s) (ALU(s)) whichmay be leveraged in performing processing with respect to any of thevariety of tasks or operations of the vehicle 900—such as processingDNNs. In addition, the SoC(s) 904 may include a floating point unit(s)(FPU(s))— or other math coprocessor or numeric coprocessor types—forperforming mathematical operations within the system. For example, theSoC(s) 904 may include one or more FPUs integrated as execution unitswithin a CPU(s) 906 and/or GPU(s) 908.

The SoC(s) 904 may include one or more accelerators 914 (e.g., hardwareaccelerators, software accelerators, or a combination thereof). Forexample, the SoC(s) 904 may include a hardware acceleration cluster thatmay include optimized hardware accelerators and/or large on-chip memory.The large on-chip memory (e.g., 4 MB of SRAM), may enable the hardwareacceleration cluster to accelerate neural networks and othercalculations. The hardware acceleration cluster may be used tocomplement the GPU(s) 908 and to off-load some of the tasks of theGPU(s) 908 (e.g., to free up more cycles of the GPU(s) 908 forperforming other tasks). As an example, the accelerator(s) 914 may beused for targeted workloads (e.g., perception, convolutional neuralnetworks (CNNs), etc.) that are stable enough to be amenable toacceleration. The term “CNN,” as used herein, may include all types ofCNNs, including region-based or regional convolutional neural networks(RCNNs) and Fast RCNNs (e.g., as used for object detection).

The accelerator(s) 914 (e.g., the hardware acceleration cluster) mayinclude a deep learning accelerator(s) (DLA). The DLA(s) may include oneor more Tensor processing units (TPUs) that may be configured to providean additional ten trillion operations per second for deep learningapplications and inferencing. The TPUs may be accelerators configuredto, and optimized for, performing image processing functions (e.g., forCNNs, RCNNs, etc.). The DLA(s) may further be optimized for a specificset of neural network types and floating point operations, as well asinferencing. The design of the DLA(s) may provide more performance permillimeter than a general-purpose GPU, and vastly exceeds theperformance of a CPU. The TPU(s) may perform several functions,including a single-instance convolution function, supporting, forexample, INT8, INT16, and FP16 data types for both features and weights,as well as post-processor functions.

The DLA(s) may quickly and efficiently execute neural networks,especially CNNs, on processed or unprocessed data for any of a varietyof functions, including, for example and without limitation: a CNN forobject identification and detection using data from camera sensors; aCNN for distance estimation using data from camera sensors; a CNN foremergency vehicle detection and identification and detection using datafrom microphones; a CNN for facial recognition and vehicle owneridentification using data from camera sensors; and/or a CNN for securityand/or safety related events.

The DLA(s) may perform any function of the GPU(s) 908, and by using aninference accelerator, for example, a designer may target either theDLA(s) or the GPU(s) 908 for any function. For example, the designer mayfocus processing of CNNs and floating point operations on the DLA(s) andleave other functions to the GPU(s) 908 and/or other accelerator(s) 914.

The accelerator(s) 914 (e.g., the hardware acceleration cluster) mayinclude a programmable vision accelerator(s) (PVA), which mayalternatively be referred to herein as a computer vision accelerator.The PVA(s) may be designed and configured to accelerate computer visionalgorithms for the advanced driver assistance systems (ADAS), autonomousdriving, and/or augmented reality (AR) and/or virtual reality (VR)applications. The PVA(s) may provide a balance between performance andflexibility. For example, each PVA(s) may include, for example andwithout limitation, any number of reduced instruction set computer(RISC) cores, direct memory access (DMA), and/or any number of vectorprocessors.

The RISC cores may interact with image sensors (e.g., the image sensorsof any of the cameras described herein), image signal processor(s),and/or the like. Each of the RISC cores may include any amount ofmemory. The RISC cores may use any of a number of protocols, dependingon the embodiment. In some examples, the RISC cores may execute areal-time operating system (RTOS). The RISC cores may be implementedusing one or more integrated circuit devices, application specificintegrated circuits (ASICs), and/or memory devices. For example, theRISC cores may include an instruction cache and/or a tightly coupledRAM.

The DMA may enable components of the PVA(s) to access the system memoryindependently of the CPU(s) 906. The DMA may support any number offeatures used to provide optimization to the PVA including, but notlimited to, supporting multi-dimensional addressing and/or circularaddressing. In some examples, the DMA may support up to six or moredimensions of addressing, which may include block width, block height,block depth, horizontal block stepping, vertical block stepping, and/ordepth stepping.

The vector processors may be programmable processors that may bedesigned to efficiently and flexibly execute programming for computervision algorithms and provide signal processing capabilities. In someexamples, the PVA may include a PVA core and two vector processingsubsystem partitions. The PVA core may include a processor subsystem,DMA engine(s) (e.g., two DMA engines), and/or other peripherals. Thevector processing subsystem may operate as the primary processing engineof the PVA, and may include a vector processing unit (VPU), aninstruction cache, and/or vector memory (e.g., VMEM). A VPU core mayinclude a digital signal processor such as, for example, a singleinstruction, multiple data (SIMD), very long instruction word (VLIW)digital signal processor. The combination of the SIMD and VLIW mayenhance throughput and speed.

Each of the vector processors may include an instruction cache and maybe coupled to dedicated memory. As a result, in some examples, each ofthe vector processors may be configured to execute independently of theother vector processors. In other examples, the vector processors thatare included in a particular PVA may be configured to employ dataparallelism. For example, in some embodiments, the plurality of vectorprocessors included in a single PVA may execute the same computer visionalgorithm, but on different regions of an image. In other examples, thevector processors included in a particular PVA may simultaneouslyexecute different computer vision algorithms, on the same image, or evenexecute different algorithms on sequential images or portions of animage. Among other things, any number of PVAs may be included in thehardware acceleration cluster and any number of vector processors may beincluded in each of the PVAs. In addition, the PVA(s) may includeadditional error correcting code (ECC) memory, to enhance overall systemsafety.

The accelerator(s) 914 (e.g., the hardware acceleration cluster) mayinclude a computer vision network on-chip and SRAM, for providing ahigh-bandwidth, low latency SRAM for the accelerator(s) 914. In someexamples, the on-chip memory may include at least 4 MB SRAM, consistingof, for example and without limitation, eight field-configurable memoryblocks, that may be accessible by both the PVA and the DLA. Each pair ofmemory blocks may include an advanced peripheral bus (APB) interface,configuration circuitry, a controller, and a multiplexer. Any type ofmemory may be used. The PVA and DLA may access the memory via a backbonethat provides the PVA and DLA with high-speed access to memory. Thebackbone may include a computer vision network on-chip thatinterconnects the PVA and the DLA to the memory (e.g., using the APB).

The computer vision network on-chip may include an interface thatdetermines, before transmission of any control signal/address/data, thatboth the PVA and the DLA provide ready and valid signals. Such aninterface may provide for separate phases and separate channels fortransmitting control signals/addresses/data, as well as burst-typecommunications for continuous data transfer. This type of interface maycomply with ISO 26262 or IEC 61508 standards, although other standardsand protocols may be used.

In some examples, the SoC(s) 904 may include a real-time ray-tracinghardware accelerator, such as described in U.S. patent application Ser.No. 16/101,232, filed on Aug. 10, 2018. The real-time ray-tracinghardware accelerator may be used to quickly and efficiently determinethe positions and extents of objects (e.g., within a world model), togenerate real-time visualization simulations, for RADAR signalinterpretation, for sound propagation synthesis and/or analysis, forsimulation of SONAR systems, for general wave propagation simulation,for comparison to LIDAR data for purposes of localization and/or otherfunctions, and/or for other uses. In some embodiments, one or more treetraversal units (TTUs) may be used for executing one or more ray-tracingrelated operations.

The accelerator(s) 914 (e.g., the hardware accelerator cluster) have awide array of uses for autonomous driving. The PVA may be a programmablevision accelerator that may be used for key processing stages in ADASand autonomous vehicles. The PVA's capabilities are a good match foralgorithmic domains needing predictable processing, at low power and lowlatency. In other words, the PVA performs well on semi-dense or denseregular computation, even on small data sets, which need predictablerun-times with low latency and low power. Thus, in the context ofplatforms for autonomous vehicles, the PVAs are designed to run classiccomputer vision algorithms, as they are efficient at object detectionand operating on integer math.

For example, according to one embodiment of the technology, the PVA isused to perform computer stereo vision. A semi-global matching-basedalgorithm may be used in some examples, although this is not intended tobe limiting. Many applications for Level 3-5 autonomous driving requiremotion estimation/stereo matching on-the-fly (e.g., structure frommotion, pedestrian recognition, lane detection, etc.). The PVA mayperform computer stereo vision function on inputs from two monocularcameras.

In some examples, the PVA may be used to perform dense optical flow.According to process raw RADAR data (e.g., using a 4D Fast FourierTransform) to provide Processed RADAR. In other examples, the PVA isused for time of flight depth processing, by processing raw time offlight data to provide processed time of flight data, for example.

The DLA may be used to run any type of network to enhance control anddriving safety, including for example, a neural network that outputs ameasure of confidence for each object detection. Such a confidence valuemay be interpreted as a probability, or as providing a relative “weight”of each detection compared to other detections. This confidence valueenables the system to make further decisions regarding which detectionsshould be considered as true positive detections rather than falsepositive detections. For example, the system may set a threshold valuefor the confidence and consider only the detections exceeding thethreshold value as true positive detections. In an automatic emergencybraking (AEB) system, false positive detections would cause the vehicleto automatically perform emergency braking, which is obviouslyundesirable. Therefore, only the most confident detections should beconsidered as triggers for AEB. The DLA may run a neural network forregressing the confidence value. The neural network may take as itsinput at least some subset of parameters, such as bounding boxdimensions, ground plane estimate obtained (e.g. from anothersubsystem), inertial measurement unit (IMU) sensor 966 output thatcorrelates with the vehicle 900 orientation, distance, 3D locationestimates of the object obtained from the neural network and/or othersensors (e.g., LIDAR sensor(s) 964 or RADAR sensor(s) 960), amongothers.

The SoC(s) 904 may include data store(s) 916 (e.g., memory). The datastore(s) 916 may be on-chip memory of the SoC(s) 904, which may storeneural networks to be executed on the GPU and/or the DLA. In someexamples, the data store(s) 916 may be large enough in capacity to storemultiple instances of neural networks for redundancy and safety. Thedata store(s) 912 may comprise L2 or L3 cache(s) 912. Reference to thedata store(s) 916 may include reference to the memory associated withthe PVA, DLA, and/or other accelerator(s) 914, as described herein.

The SoC(s) 904 may include one or more processor(s) 910 (e.g., embeddedprocessors). The processor(s) 910 may include a boot and powermanagement processor that may be a dedicated processor and subsystem tohandle boot power and management functions and related securityenforcement. The boot and power management processor may be a part ofthe SoC(s) 904 boot sequence and may provide runtime power managementservices. The boot power and management processor may provide clock andvoltage programming, assistance in system low power state transitions,management of SoC(s) 904 thermals and temperature sensors, and/ormanagement of the SoC(s) 904 power states. Each temperature sensor maybe implemented as a ring-oscillator whose output frequency isproportional to temperature, and the SoC(s) 904 may use thering-oscillators to detect temperatures of the CPU(s) 906, GPU(s) 908,and/or accelerator(s) 914. If temperatures are determined to exceed athreshold, the boot and power management processor may enter atemperature fault routine and put the SoC(s) 904 into a lower powerstate and/or put the vehicle 900 into a chauffeur to safe stop mode(e.g., bring the vehicle 900 to a safe stop).

The processor(s) 910 may further include a set of embedded processorsthat may serve as an audio processing engine. The audio processingengine may be an audio subsystem that enables full hardware support formulti-channel audio over multiple interfaces, and a broad and flexiblerange of audio I/O interfaces. In some examples, the audio processingengine is a dedicated processor core with a digital signal processorwith dedicated RAM.

The processor(s) 910 may further include an always on processor enginethat may provide necessary hardware features to support low power sensormanagement and wake use cases. The always on processor engine mayinclude a processor core, a tightly coupled RAM, supporting peripherals(e.g., timers and interrupt controllers), various I/O controllerperipherals, and routing logic.

The processor(s) 910 may further include a safety cluster engine thatincludes a dedicated processor subsystem to handle safety management forautomotive applications. The safety cluster engine may include two ormore processor cores, a tightly coupled RAM, support peripherals (e.g.,timers, an interrupt controller, etc.), and/or routing logic. In asafety mode, the two or more cores may operate in a lockstep mode andfunction as a single core with comparison logic to detect anydifferences between their operations.

The processor(s) 910 may further include a real-time camera engine thatmay include a dedicated processor subsystem for handling real-timecamera management.

The processor(s) 910 may further include a high-dynamic range signalprocessor that may include an image signal processor that is a hardwareengine that is part of the camera processing pipeline.

The processor(s) 910 may include a video image compositor that may be aprocessing block (e.g., implemented on a microprocessor) that implementsvideo post-processing functions needed by a video playback applicationto produce the final image for the player window. The video imagecompositor may perform lens distortion correction on wide-view camera(s)970, surround camera(s) 974, and/or on in-cabin monitoring camerasensors. In-cabin monitoring camera sensor is preferably monitored by aneural network running on another instance of the Advanced SoC,configured to identify in cabin events and respond accordingly. Anin-cabin system may perform lip reading to activate cellular service andplace a phone call, dictate emails, change the vehicle's destination,activate or change the vehicle's infotainment system and settings, orprovide voice-activated web surfing. Certain functions are available tothe driver only when the vehicle is operating in an autonomous mode, andare disabled otherwise.

The video image compositor may include enhanced temporal noise reductionfor both spatial and temporal noise reduction. For example, where motionoccurs in a video, the noise reduction weights spatial informationappropriately, decreasing the weight of information provided by adjacentframes. Where an image or portion of an image does not include motion,the temporal noise reduction performed by the video image compositor mayuse information from the previous image to reduce noise in the currentimage.

The video image compositor may also be configured to perform stereorectification on input stereo lens frames. The video image compositormay further be used for user interface composition when the operatingsystem desktop is in use, and the GPU(s) 908 is not required tocontinuously render new surfaces. Even when the GPU(s) 908 is powered onand active doing 3D rendering, the video image compositor may be used tooffload the GPU(s) 908 to improve performance and responsiveness.

The SoC(s) 904 may further include a mobile industry processor interface(MIPI) camera serial interface for receiving video and input fromcameras, a high-speed interface, and/or a video input block that may beused for camera and related pixel input functions. The SoC(s) 904 mayfurther include an input/output controller(s) that may be controlled bysoftware and may be used for receiving I/O signals that are uncommittedto a specific role.

The SoC(s) 904 may further include a broad range of peripheralinterfaces to enable communication with peripherals, audio codecs, powermanagement, and/or other devices. The SoC(s) 904 may be used to processdata from cameras (e.g., connected over Gigabit Multimedia Serial Linkand Ethernet), sensors (e.g., LIDAR sensor(s) 964, RADAR sensor(s) 960,etc. that may be connected over Ethernet), data from bus 902 (e.g.,speed of vehicle 900, steering wheel position, etc.), data from GNSSsensor(s) 958 (e.g., connected over Ethernet or CAN bus). The SoC(s) 904may further include dedicated high-performance mass storage controllersthat may include their own DMA engines, and that may be used to free theCPU(s) 906 from routine data management tasks.

The SoC(s) 904 may be an end-to-end platform with a flexiblearchitecture that spans automation levels 3-5, thereby providing acomprehensive functional safety architecture that leverages and makesefficient use of computer vision and ADAS techniques for diversity andredundancy, provides a platform for a flexible, reliable drivingsoftware stack, along with deep learning tools. The SoC(s) 904 may befaster, more reliable, and even more energy-efficient andspace-efficient than conventional systems. For example, theaccelerator(s) 914, when combined with the CPU(s) 906, the GPU(s) 908,and the data store(s) 916, may provide for a fast, efficient platformfor level 3-5 autonomous vehicles.

The technology thus provides capabilities and functionality that cannotbe achieved by conventional systems. For example, computer visionalgorithms may be executed on CPUs, which may be configured usinghigh-level programming language, such as the C programming language, toexecute a wide variety of processing algorithms across a wide variety ofvisual data. However, CPUs are oftentimes unable to meet the performancerequirements of many computer vision applications, such as those relatedto execution time and power consumption, for example. In particular,many CPUs are unable to execute complex object detection algorithms inreal-time, which is a requirement of in-vehicle ADAS applications, and arequirement for practical Level 3-5 autonomous vehicles.

In contrast to conventional systems, by providing a CPU complex, GPUcomplex, and a hardware acceleration cluster, the technology describedherein allows for multiple neural networks to be performedsimultaneously and/or sequentially, and for the results to be combinedtogether to enable Level 3-5 autonomous driving functionality. Forexample, a CNN executing on the DLA or dGPU (e.g., the GPU(s) 920) mayinclude a text and word recognition, allowing the supercomputer to readand understand traffic signs, including signs for which the neuralnetwork has not been specifically trained. The DLA may further include aneural network that is able to identify, interpret, and providessemantic understanding of the sign, and to pass that semanticunderstanding to the path planning modules running on the CPU Complex.

As another example, multiple neural networks may be run simultaneously,as is required for Level 3, 4, or 5 driving. For example, a warning signconsisting of “Caution: flashing lights indicate icy conditions,” alongwith an electric light, may be independently or collectively interpretedby several neural networks. The sign itself may be identified as atraffic sign by a first deployed neural network (e.g., a neural networkthat has been trained), the text “Flashing lights indicate icyconditions” may be interpreted by a second deployed neural network,which informs the vehicle's path planning software (preferably executingon the CPU Complex) that when flashing lights are detected, icyconditions exist. The flashing light may be identified by operating athird deployed neural network over multiple frames, informing thevehicle's path-planning software of the presence (or absence) offlashing lights. All three neural networks may run simultaneously, suchas within the DLA and/or on the GPU(s) 908.

In some examples, a CNN for facial recognition and vehicle owneridentification may use data from camera sensors to identify the presenceof an authorized driver and/or owner of the vehicle 900. The always onsensor processing engine may be used to unlock the vehicle when theowner approaches the driver door and turn on the lights, and, insecurity mode, to disable the vehicle when the owner leaves the vehicle.In this way, the SoC(s) 904 provide for security against theft and/orcarjacking.

In another example, a CNN for emergency vehicle detection andidentification may use data from microphones 996 to detect and identifyemergency vehicle sirens. In contrast to conventional systems, that usegeneral classifiers to detect sirens and manually extract features, theSoC(s) 904 use the CNN for classifying environmental and urban sounds,as well as classifying visual data. In a preferred embodiment, the CNNrunning on the DLA is trained to identify the relative closing speed ofthe emergency vehicle (e.g., by using the Doppler Effect). The CNN mayalso be trained to identify emergency vehicles specific to the localarea in which the vehicle is operating, as identified by GNSS sensor(s)958. Thus, for example, when operating in Europe the CNN will seek todetect European sirens, and when in the United States the CNN will seekto identify only North American sirens. Once an emergency vehicle isdetected, a control program may be used to execute an emergency vehiclesafety routine, slowing the vehicle, pulling over to the side of theroad, parking the vehicle, and/or idling the vehicle, with theassistance of ultrasonic sensors 962, until the emergency vehicle(s)passes.

The vehicle may include a CPU(s) 918 (e.g., discrete CPU(s), ordCPU(s)), that may be coupled to the SoC(s) 904 via a high-speedinterconnect (e.g., PCIe). The CPU(s) 918 may include an X86 processor,for example. The CPU(s) 918 may be used to perform any of a variety offunctions, including arbitrating potentially inconsistent resultsbetween ADAS sensors and the SoC(s) 904, and/or monitoring the statusand health of the controller(s) 936 and/or infotainment SoC 930, forexample.

The vehicle 900 may include a GPU(s) 920 (e.g., discrete GPU(s), ordGPU(s)), that may be coupled to the SoC(s) 904 via a high-speedinterconnect (e.g., NVIDIA's NVLINK). The GPU(s) 920 may provideadditional artificial intelligence functionality, such as by executingredundant and/or different neural networks, and may be used to trainand/or update neural networks based on input (e.g., sensor data) fromsensors of the vehicle 900.

The vehicle 900 may further include the network interface 924 which mayinclude one or more wireless antennas 926 (e.g., one or more wirelessantennas for different communication protocols, such as a cellularantenna, a Bluetooth antenna, etc.). The network interface 924 may beused to enable wireless connectivity over the Internet with the cloud(e.g., with the server(s) 978 and/or other network devices), with othervehicles, and/or with computing devices (e.g., client devices ofpassengers). To communicate with other vehicles, a direct link may beestablished between the two vehicles and/or an indirect link may beestablished (e.g., across networks and over the Internet). Direct linksmay be provided using a vehicle-to-vehicle communication link. Thevehicle-to-vehicle communication link may provide the vehicle 900information about vehicles in proximity to the vehicle 900 (e.g.,vehicles in front of, on the side of, and/or behind the vehicle 900).This functionality may be part of a cooperative adaptive cruise controlfunctionality of the vehicle 900.

The network interface 924 may include a SoC that provides modulation anddemodulation functionality and enables the controller(s) 936 tocommunicate over wireless networks. The network interface 924 mayinclude a radio frequency front-end for up-conversion from baseband toradio frequency, and down conversion from radio frequency to baseband.The frequency conversions may be performed through well-known processes,and/or may be performed using super-heterodyne processes. In someexamples, the radio frequency front end functionality may be provided bya separate chip. The network interface may include wirelessfunctionality for communicating over LTE, WCDMA, UMTS, GSM, CDMA2000,Bluetooth, Bluetooth LE, Wi-Fi, Z-Wave, ZigBee, LoRaWAN, and/or otherwireless protocols.

The vehicle 900 may further include data store(s) 928 which may includeoff-chip (e.g., off the SoC(s) 904) storage. The data store(s) 928 mayinclude one or more storage elements including RAM, SRAM, DRAM, VRAM,Flash, hard disks, and/or other components and/or devices that may storeat least one bit of data.

The vehicle 900 may further include GNSS sensor(s) 958. The GNSSsensor(s) 958 (e.g., GPS, assisted GPS sensors, differential GPS (DGPS)sensors, etc.), to assist in mapping, perception, occupancy gridgeneration, and/or path planning functions. Any number of GNSS sensor(s)958 may be used, including, for example and without limitation, a GPSusing a USB connector with an Ethernet to Serial (RS-232) bridge.

The vehicle 900 may further include RADAR sensor(s) 960. The RADARsensor(s) 960 may be used by the vehicle 900 for long-range vehicledetection, even in darkness and/or severe weather conditions. RADARfunctional safety levels may be ASIL B. The RADAR sensor(s) 960 may usethe CAN and/or the bus 902 (e.g., to transmit data generated by theRADAR sensor(s) 960) for control and to access object tracking data,with access to Ethernet to access raw data in some examples. A widevariety of RADAR sensor types may be used. For example, and withoutlimitation, the RADAR sensor(s) 960 may be suitable for front, rear, andside RADAR use. In some example, Pulse Doppler RADAR sensor(s) are used.

The RADAR sensor(s) 960 may include different configurations, such aslong range with narrow field of view, short range with wide field ofview, short range side coverage, etc. In some examples, long-range RADARmay be used for adaptive cruise control functionality. The long-rangeRADAR systems may provide a broad field of view realized by two or moreindependent scans, such as within a 250 m range. The RADAR sensor(s) 960may help in distinguishing between static and moving objects, and may beused by ADAS systems for emergency brake assist and forward collisionwarning. Long-range RADAR sensors may include monostatic multimodalRADAR with multiple (e.g., six or more) fixed RADAR antennae and ahigh-speed CAN and FlexRay interface. In an example with six antennae,the central four antennae may create a focused beam pattern, designed torecord the vehicle's 900 surroundings at higher speeds with minimalinterference from traffic in adjacent lanes. The other two antennae mayexpand the field of view, making it possible to quickly detect vehiclesentering or leaving the vehicle's 900 lane.

Mid-range RADAR systems may include, as an example, a range of up to 960m (front) or 80 m (rear), and a field of view of up to 42 degrees(front) or 950 degrees (rear). Short-range RADAR systems may include,without limitation, RADAR sensors designed to be installed at both endsof the rear bumper. When installed at both ends of the rear bumper, sucha RADAR sensor systems may create two beams that constantly monitor theblind spot in the rear and next to the vehicle.

Short-range RADAR systems may be used in an ADAS system for blind spotdetection and/or lane change assist.

The vehicle 900 may further include ultrasonic sensor(s) 962. Theultrasonic sensor(s) 962, which may be positioned at the front, back,and/or the sides of the vehicle 900, may be used for park assist and/orto create and update an occupancy grid. A wide variety of ultrasonicsensor(s) 962 may be used, and different ultrasonic sensor(s) 962 may beused for different ranges of detection (e.g., 2.5 m, 4 m). Theultrasonic sensor(s) 962 may operate at functional safety levels of ASILB.

The vehicle 900 may include LIDAR sensor(s) 964. The LIDAR sensor(s) 964may be used for object and pedestrian detection, emergency braking,collision avoidance, and/or other functions. The LIDAR sensor(s) 964 maybe functional safety level ASIL B. In some examples, the vehicle 900 mayinclude multiple LIDAR sensors 964 (e.g., two, four, six, etc.) that mayuse Ethernet (e.g., to provide data to a Gigabit Ethernet switch).

In some examples, the LIDAR sensor(s) 964 may be capable of providing alist of objects and their distances for a 360-degree field of view.Commercially available LIDAR sensor(s) 964 may have an advertised rangeof approximately 900 m, with an accuracy of 2 cm-3 cm, and with supportfor a 900 Mbps Ethernet connection, for example. In some examples, oneor more non-protruding LIDAR sensors 964 may be used. In such examples,the LIDAR sensor(s) 964 may be implemented as a small device that may beembedded into the front, rear, sides, and/or corners of the vehicle 900.The LIDAR sensor(s) 964, in such examples, may provide up to a120-degree horizontal and 35-degree vertical field-of-view, with a 200 mrange even for low-reflectivity objects. Front-mounted LIDAR sensor(s)964 may be configured for a horizontal field of view between 45 degreesand 135 degrees.

In some examples, LIDAR technologies, such as 3D flash LIDAR, may alsobe used. 3D Flash LIDAR uses a flash of a laser as a transmissionsource, to illuminate vehicle surroundings up to approximately 200 m. Aflash LIDAR unit includes a receptor, which records the laser pulsetransit time and the reflected light on each pixel, which in turncorresponds to the range from the vehicle to the objects. Flash LIDARmay allow for highly accurate and distortion-free images of thesurroundings to be generated with every laser flash. In some examples,four flash LIDAR sensors may be deployed, one at each side of thevehicle 900. Available 3D flash LIDAR systems include a solid-state 3Dstaring array LIDAR camera with no moving parts other than a fan (e.g.,a non-scanning LIDAR device). The flash LIDAR device may use a 5nanosecond class I (eye-safe) laser pulse per frame and may capture thereflected laser light in the form of 3D range point clouds andco-registered intensity data. By using flash LIDAR, and because flashLIDAR is a solid-state device with no moving parts, the LIDAR sensor(s)964 may be less susceptible to motion blur, vibration, and/or shock.

The vehicle may further include IMU sensor(s) 966. The IMU sensor(s) 966may be located at a center of the rear axle of the vehicle 900, in someexamples. The IMU sensor(s) 966 may include, for example and withoutlimitation, an accelerometer(s), a magnetometer(s), a gyroscope(s), amagnetic compass(es), and/or other sensor types. In some examples, suchas in six-axis applications, the IMU sensor(s) 966 may includeaccelerometers and gyroscopes, while in nine-axis applications, the IMUsensor(s) 966 may include accelerometers, gyroscopes, and magnetometers.

In some embodiments, the IMU sensor(s) 966 may be implemented as aminiature, high performance GPS-Aided Inertial Navigation System(GPS/INS) that combines micro-electro-mechanical systems (MEMS) inertialsensors, a high-sensitivity GPS receiver, and advanced Kalman filteringalgorithms to provide estimates of position, velocity, and attitude. Assuch, in some examples, the IMU sensor(s) 966 may enable the vehicle 900to estimate heading without requiring input from a magnetic sensor bydirectly observing and correlating the changes in velocity from GPS tothe IMU sensor(s) 966. In some examples, the IMU sensor(s) 966 and theGNSS sensor(s) 958 may be combined in a single integrated unit.

The vehicle may include microphone(s) 996 placed in and/or around thevehicle 900. The microphone(s) 996 may be used for emergency vehicledetection and identification, among other things.

The vehicle may further include any number of camera types, includingstereo camera(s) 968, wide-view camera(s) 970, infrared camera(s) 972,surround camera(s) 974, long-range and/or mid-range camera(s) 998,and/or other camera types. The cameras may be used to capture image dataaround an entire periphery of the vehicle 900. The types of cameras useddepends on the embodiments and requirements for the vehicle 900, and anycombination of camera types may be used to provide the necessarycoverage around the vehicle 900. In addition, the number of cameras maydiffer depending on the embodiment. For example, the vehicle may includesix cameras, seven cameras, ten cameras, twelve cameras, and/or anothernumber of cameras. The cameras may support, as an example and withoutlimitation, Gigabit Multimedia Serial Link (GMSL) and/or GigabitEthernet. Each of the camera(s) is described with more detail hereinwith respect to FIG. 9A and FIG. 9B.

The vehicle 900 may further include vibration sensor(s) 942. Thevibration sensor(s) 942 may measure vibrations of components of thevehicle, such as the axle(s). For example, changes in vibrations mayindicate a change in road surfaces. In another example, when two or morevibration sensors 942 are used, the differences between the vibrationsmay be used to determine friction or slippage of the road surface (e.g.,when the difference in vibration is between a power-driven axle and afreely rotating axle).

The vehicle 900 may include an ADAS system 938. The ADAS system 938 mayinclude a SoC, in some examples. The ADAS system 938 may includeautonomous/adaptive/automatic cruise control (ACC), cooperative adaptivecruise control (CACC), forward crash warning (FCW), automatic emergencybraking (AEB), lane departure warnings (LDW), lane keep assist (LKA),blind spot warning (BSW), rear cross-traffic warning (RCTW), collisionwarning systems (CWS), lane centering (LC), and/or other features andfunctionality.

The ACC systems may use RADAR sensor(s) 960, LIDAR sensor(s) 964, and/ora camera(s). The ACC systems may include longitudinal ACC and/or lateralACC. Longitudinal ACC monitors and controls the distance to the vehicleimmediately ahead of the vehicle 900 and automatically adjust thevehicle speed to maintain a safe distance from vehicles ahead. LateralACC performs distance keeping, and advises the vehicle 900 to changelanes when necessary. Lateral ACC is related to other ADAS applicationssuch as LCA and CWS.

CACC uses information from other vehicles that may be received via thenetwork interface 924 and/or the wireless antenna(s) 926 from othervehicles via a wireless link, or indirectly, over a network connection(e.g., over the Internet). Direct links may be provided by avehicle-to-vehicle (V2V) communication link, while indirect links may beinfrastructure-to-vehicle (I2V) communication link. In general, the V2Vcommunication concept provides information about the immediatelypreceding vehicles (e.g., vehicles immediately ahead of and in the samelane as the vehicle 900), while the I2V communication concept providesinformation about traffic further ahead. CACC systems may include eitheror both I2V and V2V information sources. Given the information of thevehicles ahead of the vehicle 900, CACC may be more reliable and it haspotential to improve traffic flow smoothness and reduce congestion onthe road.

FCW systems are designed to alert the driver to a hazard, so that thedriver may take corrective action. FCW systems use a front-facing cameraand/or RADAR sensor(s) 960, coupled to a dedicated processor, DSP, FPGA,and/or ASIC, that is electrically coupled to driver feedback, such as adisplay, speaker, and/or vibrating component. FCW systems may provide awarning, such as in the form of a sound, visual warning, vibrationand/or a quick brake pulse.

AEB systems detect an impending forward collision with another vehicleor other object, and may automatically apply the brakes if the driverdoes not take corrective action within a specified time or distanceparameter. AEB systems may use front-facing camera(s) and/or RADARsensor(s) 960, coupled to a dedicated processor, DSP, FPGA, and/or ASIC.When the AEB system detects a hazard, it typically first alerts thedriver to take corrective action to avoid the collision and, if thedriver does not take corrective action, the AEB system may automaticallyapply the brakes in an effort to prevent, or at least mitigate, theimpact of the predicted collision. AEB systems, may include techniquessuch as dynamic brake support and/or crash imminent braking.

LDW systems provide visual, audible, and/or tactile warnings, such assteering wheel or seat vibrations, to alert the driver when the vehicle900 crosses lane markings. A LDW system does not activate when thedriver indicates an intentional lane departure, by activating a turnsignal. LDW systems may use front-side facing cameras, coupled to adedicated processor, DSP, FPGA, and/or ASIC, that is electricallycoupled to driver feedback, such as a display, speaker, and/or vibratingcomponent.

LKA systems are a variation of LDW systems. LKA systems provide steeringinput or braking to correct the vehicle 900 if the vehicle 900 starts toexit the lane.

BSW systems detects and warn the driver of vehicles in an automobile'sblind spot. BSW systems may provide a visual, audible, and/or tactilealert to indicate that merging or changing lanes is unsafe. The systemmay provide an additional warning when the driver uses a turn signal.BSW systems may use rear-side facing camera(s) and/or RADAR sensor(s)960, coupled to a dedicated processor, DSP, FPGA, and/or ASIC, that iselectrically coupled to driver feedback, such as a display, speaker,and/or vibrating component.

RCTW systems may provide visual, audible, and/or tactile notificationwhen an object is detected outside the rear-camera range when thevehicle 900 is backing up. Some RCTW systems include AEB to ensure thatthe vehicle brakes are applied to avoid a crash. RCTW systems may useone or more rear-facing RADAR sensor(s) 960, coupled to a dedicatedprocessor, DSP, FPGA, and/or ASIC, that is electrically coupled todriver feedback, such as a display, speaker, and/or vibrating component.

Conventional ADAS systems may be prone to false positive results whichmay be annoying and distracting to a driver, but typically are notcatastrophic, because the ADAS systems alert the driver and allow thedriver to decide whether a safety condition truly exists and actaccordingly. However, in an autonomous vehicle 900, the vehicle 900itself must, in the case of conflicting results, decide whether to heedthe result from a primary computer or a secondary computer (e.g., afirst controller 936 or a second controller 936). For example, in someembodiments, the ADAS system 938 may be a backup and/or secondarycomputer for providing perception information to a backup computerrationality module. The backup computer rationality monitor may run aredundant diverse software on hardware components to detect faults inperception and dynamic driving tasks. Outputs from the ADAS system 938may be provided to a supervisory MCU. If outputs from the primarycomputer and the secondary computer conflict, the supervisory MCU mustdetermine how to reconcile the conflict to ensure safe operation.

In some examples, the primary computer may be configured to provide thesupervisory MCU with a confidence score, indicating the primarycomputer's confidence in the chosen result. If the confidence scoreexceeds a threshold, the supervisory MCU may follow the primarycomputer's direction, regardless of whether the secondary computerprovides a conflicting or inconsistent result. Where the confidencescore does not meet the threshold, and where the primary and secondarycomputer indicate different results (e.g., the conflict), thesupervisory MCU may arbitrate between the computers to determine theappropriate outcome.

The supervisory MCU may be configured to run a neural network(s) that istrained and configured to determine, based on outputs from the primarycomputer and the secondary computer, conditions under which thesecondary computer provides false alarms. Thus, the neural network(s) inthe supervisory MCU may learn when the secondary computer's output maybe trusted, and when it cannot. For example, when the secondary computeris a RADAR-based FCW system, a neural network(s) in the supervisory MCUmay learn when the FCW system is identifying metallic objects that arenot, in fact, hazards, such as a drainage grate or manhole cover thattriggers an alarm. Similarly, when the secondary computer is acamera-based LDW system, a neural network in the supervisory MCU maylearn to override the LDW when bicyclists or pedestrians are present anda lane departure is, in fact, the safest maneuver. In embodiments thatinclude a neural network(s) running on the supervisory MCU, thesupervisory MCU may include at least one of a DLA or GPU suitable forrunning the neural network(s) with associated memory. In preferredembodiments, the supervisory MCU may comprise and/or be included as acomponent of the SoC(s) 904.

In other examples, ADAS system 938 may include a secondary computer thatperforms ADAS functionality using traditional rules of computer vision.As such, the secondary computer may use classic computer vision rules(if-then), and the presence of a neural network(s) in the supervisoryMCU may improve reliability, safety and performance. For example, thediverse implementation and intentional non-identity makes the overallsystem more fault-tolerant, especially to faults caused by software (orsoftware-hardware interface) functionality. For example, if there is asoftware bug or error in the software running on the primary computer,and the non-identical software code running on the secondary computerprovides the same overall result, the supervisory MCU may have greaterconfidence that the overall result is correct, and the bug in softwareor hardware on primary computer is not causing material error.

In some examples, the output of the ADAS system 938 may be fed into theprimary computer's perception block and/or the primary computer'sdynamic driving task block. For example, if the ADAS system 938indicates a forward crash warning due to an object immediately ahead,the perception block may use this information when identifying objects.In other examples, the secondary computer may have its own neuralnetwork which is trained and thus reduces the risk of false positives,as described herein.

The vehicle 900 may further include the infotainment SoC 930 (e.g., anin-vehicle infotainment system (IVI)). Although illustrated anddescribed as a SoC, the infotainment system may not be a SoC, and mayinclude two or more discrete components. The infotainment SoC 930 mayinclude a combination of hardware and software that may be used toprovide audio (e.g., music, a personal digital assistant, navigationalinstructions, news, radio, etc.), video (e.g., TV, movies, streaming,etc.), phone (e.g., hands-free calling), network connectivity (e.g.,LTE, Wi-Fi, etc.), and/or information services (e.g., navigationsystems, rear-parking assistance, a radio data system, vehicle relatedinformation such as fuel level, total distance covered, brake fuellevel, oil level, door open/close, air filter information, etc.) to thevehicle 900. For example, the infotainment SoC 930 may radios, diskplayers, navigation systems, video players, USB and Bluetoothconnectivity, carputers, in-car entertainment, Wi-Fi, steering wheelaudio controls, hands free voice control, a heads-up display (HUD), anHMI display 934, a telematics device, a control panel (e.g., forcontrolling and/or interacting with various components, features, and/orsystems), and/or other components. The infotainment SoC 930 may furtherbe used to provide information (e.g., visual and/or audible) to auser(s) of the vehicle, such as information from the ADAS system 938,autonomous driving information such as planned vehicle maneuvers,trajectories, surrounding environment information (e.g., intersectioninformation, vehicle information, road information, etc.), and/or otherinformation.

The infotainment SoC 930 may include GPU functionality. The infotainmentSoC 930 may communicate over the bus 902 (e.g., CAN bus, Ethernet, etc.)with other devices, systems, and/or components of the vehicle 900. Insome examples, the infotainment SoC 930 may be coupled to a supervisoryMCU such that the GPU of the infotainment system may perform someself-driving functions in the event that the primary controller(s) 936(e.g., the primary and/or backup computers of the vehicle 900) fail. Insuch an example, the infotainment SoC 930 may put the vehicle 900 into achauffeur to safe stop mode, as described herein.

The vehicle 900 may further include an instrument cluster 932 (e.g., adigital dash, an electronic instrument cluster, a digital instrumentpanel, etc.). The instrument cluster 932 may include a controller and/orsupercomputer (e.g., a discrete controller or supercomputer). Theinstrument cluster 932 may include a set of instrumentation such as aspeedometer, fuel level, oil pressure, tachometer, odometer, turnindicators, gearshift position indicator, seat belt warning light(s),parking-brake warning light(s), engine-malfunction light(s), airbag(SRS) system information, lighting controls, safety system controls,navigation information, etc. In some examples, information may bedisplayed and/or shared among the infotainment SoC 930 and theinstrument cluster 932. In other words, the instrument cluster 932 maybe included as part of the infotainment SoC 930, or vice versa.

FIG. 9D is a system diagram for communication between cloud-basedserver(s) and the example autonomous vehicle 900 of FIG. 9A, inaccordance with some embodiments of the present disclosure. The system976 may include server(s) 978, network(s) 990, and vehicles, includingthe vehicle 900. The server(s) 978 may include a plurality of GPUs984(A)-984(H) (collectively referred to herein as GPUs 984), PCIeswitches 982(A)-982(H) (collectively referred to herein as PCIe switches982), and/or CPUs 980(A)-980(B) (collectively referred to herein as CPUs980). The GPUs 984, the CPUs 980, and the PCIe switches may beinterconnected with high-speed interconnects such as, for example andwithout limitation, NVLink interfaces 988 developed by NVIDIA and/orPCIe connections 986. In some examples, the GPUs 984 are connected viaNVLink and/or NVSwitch SoC and the GPUs 984 and the PCIe switches 982are connected via PCIe interconnects. Although eight GPUs 984, two CPUs980, and two PCIe switches are illustrated, this is not intended to belimiting. Depending on the embodiment, each of the server(s) 978 mayinclude any number of GPUs 984, CPUs 980, and/or PCIe switches. Forexample, the server(s) 978 may each include eight, sixteen, thirty-two,and/or more GPUs 984.

The server(s) 978 may receive, over the network(s) 990 and from thevehicles, image data representative of images showing unexpected orchanged road conditions, such as recently commenced road-work. Theserver(s) 978 may transmit, over the network(s) 990 and to the vehicles,neural networks 992, updated neural networks 992, and/or map information994, including information regarding traffic and road conditions. Theupdates to the map information 994 may include updates for the HD map922, such as information regarding construction sites, potholes,detours, flooding, and/or other obstructions. In some examples, theneural networks 992, the updated neural networks 992, and/or the mapinformation 994 may have resulted from new training and/or experiencesrepresented in data received from any number of vehicles in theenvironment, and/or based on training performed at a datacenter (e.g.,using the server(s) 978 and/or other servers).

The server(s) 978 may be used to train machine learning models (e.g.,neural networks) based on training data. The training data may begenerated by the vehicles, and/or may be generated in a simulation(e.g., using a game engine). In some examples, the training data istagged (e.g., where the neural network benefits from supervisedlearning) and/or undergoes other pre-processing, while in other examplesthe training data is not tagged and/or pre-processed (e.g., where theneural network does not require supervised learning). Training may beexecuted according to any one or more classes of machine learningtechniques, including, without limitation, classes such as: supervisedtraining, semi-supervised training, unsupervised training,self-learning, reinforcement learning, federated learning, transferlearning, feature learning (including principal component and clusteranalyses), multi-linear subspace learning, manifold learning,representation learning (including spare dictionary learning),rule-based machine learning, anomaly detection, and any variants orcombinations therefor. Once the machine learning models are trained, themachine learning models may be used by the vehicles (e.g., transmittedto the vehicles over the network(s) 990, and/or the machine learningmodels may be used by the server(s) 978 to remotely monitor thevehicles.

In some examples, the server(s) 978 may receive data from the vehiclesand apply the data to up-to-date real-time neural networks for real-timeintelligent inferencing. The server(s) 978 may include deep-learningsupercomputers and/or dedicated AI computers powered by GPU(s) 984, suchas a DGX and DGX Station machines developed by NVIDIA. However, in someexamples, the server(s) 978 may include deep learning infrastructurethat use only CPU-powered datacenters.

The deep-learning infrastructure of the server(s) 978 may be capable offast, real-time inferencing, and may use that capability to evaluate andverify the health of the processors, software, and/or associatedhardware in the vehicle 900. For example, the deep-learninginfrastructure may receive periodic updates from the vehicle 900, suchas a sequence of images and/or objects that the vehicle 900 has locatedin that sequence of images (e.g., via computer vision and/or othermachine learning object classification techniques). The deep-learninginfrastructure may run its own neural network to identify the objectsand compare them with the objects identified by the vehicle 900 and, ifthe results do not match and the infrastructure concludes that the AI inthe vehicle 900 is malfunctioning, the server(s) 978 may transmit asignal to the vehicle 900 instructing a fail-safe computer of thevehicle 900 to assume control, notify the passengers, and complete asafe parking maneuver.

For inferencing, the server(s) 978 may include the GPU(s) 984 and one ormore programmable inference accelerators (e.g., NVIDIA's TensorRT). Thecombination of GPU-powered servers and inference acceleration may makereal-time responsiveness possible. In other examples, such as whereperformance is less critical, servers powered by CPUs, FPGAs, and otherprocessors may be used for inferencing.

Example Computing Device

FIG. 10 is a block diagram of an example computing device(s) 1000suitable for use in implementing some embodiments of the presentdisclosure, such as but not limited to the stereo disparity machinelearning hazard detector 102. Computing device 1000 may include aninterconnect system 1002 that directly or indirectly couples thefollowing devices: memory 1004, one or more central processing units(CPUs) 1006, one or more graphics processing units (GPUs) 1008, acommunication interface 1010, input/output (I/O) ports 1012,input/output components 1014, a power supply 1016, one or morepresentation components 1018 (e.g., display(s)), and one or more logicunits 1020. In at least one embodiment, the computing device(s) 1000 maycomprise one or more virtual machines (VMs), and/or any of thecomponents thereof may comprise virtual components (e.g., virtualhardware components). For non-limiting examples, one or more of the GPUs1008 may comprise one or more vGPUs, one or more of the CPUs 1006 maycomprise one or more vCPUs, and/or one or more of the logic units 1020may comprise one or more virtual logic units. As such, a computingdevice(s) 1000 may include discrete components (e.g., a full GPUdedicated to the computing device 1000), virtual components (e.g., aportion of a GPU dedicated to the computing device 1000), or acombination thereof.

Although the various blocks of FIG. 10 are shown as connected via theinterconnect system 1002 with lines, this is not intended to be limitingand is for clarity only. For example, in some embodiments, apresentation component 1018, such as a display device, may be consideredan I/O component 1014 (e.g., if the display is a touch screen). Asanother example, the CPUs 1006 and/or GPUs 1008 may include memory(e.g., the memory 1004 may be representative of a storage device inaddition to the memory of the GPUs 1008, the CPUs 1006, and/or othercomponents). In other words, the computing device of FIG. 10 is merelyillustrative. Distinction is not made between such categories as“workstation,” “server,” “laptop,” “desktop,” “tablet,” “client device,”“mobile device,” “hand-held device,” “game console,” “electronic controlunit (ECU),” “virtual reality system,” and/or other device or systemtypes, as all are contemplated within the scope of the computing deviceof FIG. 10 .

The interconnect system 1002 may represent one or more links or busses,such as an address bus, a data bus, a control bus, or a combinationthereof. The interconnect system 1002 may include one or more bus orlink types, such as an industry standard architecture (ISA) bus, anextended industry standard architecture (EISA) bus, a video electronicsstandards association (VESA) bus, a peripheral component interconnect(PCI) bus, a peripheral component interconnect express (PCIe) bus,and/or another type of bus or link. In some embodiments, there aredirect connections between components. As an example, the CPU 1006 maybe directly connected to the memory 1004. Further, the CPU 1006 may bedirectly connected to the GPU 1008. Where there is direct, orpoint-to-point connection between components, the interconnect system1002 may include a PCIe link to carry out the connection. In theseexamples, a PCI bus need not be included in the computing device 1000.

The memory 1004 may include any of a variety of computer-readable media.The computer-readable media may be any available media that may beaccessed by the computing device 1000. The computer-readable media mayinclude both volatile and nonvolatile media, and removable andnon-removable media. By way of example, and not limitation, thecomputer-readable media may comprise computer-storage media andcommunication media.

The computer-storage media may include both volatile and nonvolatilemedia and/or removable and non-removable media implemented in any methodor technology for storage of information such as computer-readableinstructions, data structures, program modules, and/or other data types.For example, the memory 1004 may store computer-readable instructions(e.g., that represent a program(s) and/or a program element(s), such asan operating system. Computer-storage media may include, but is notlimited to, RAM, ROM, EEPROM, flash memory or other memory technology,CD-ROM, digital versatile disks (DVD) or other optical disk storage,magnetic cassettes, magnetic tape, magnetic disk storage or othermagnetic storage devices, or any other medium which may be used to storethe desired information and which may be accessed by computing device1000. As used herein, computer storage media does not comprise signalsper se.

The computer storage media may embody computer-readable instructions,data structures, program modules, and/or other data types in a modulateddata signal such as a carrier wave or other transport mechanism andincludes any information delivery media. The term “modulated datasignal” may refer to a signal that has one or more of itscharacteristics set or changed in such a manner as to encode informationin the signal. By way of example, and not limitation, the computerstorage media may include wired media such as a wired network ordirect-wired connection, and wireless media such as acoustic, RF,infrared and other wireless media. Combinations of any of the aboveshould also be included within the scope of computer-readable media.

The CPU(s) 1006 may be configured to execute at least some of thecomputer-readable instructions to control one or more components of thecomputing device 1000 to perform one or more of the methods and/orprocesses described herein. The CPU(s) 1006 may each include one or morecores (e.g., one, two, four, eight, twenty-eight, seventy-two, etc.)that are capable of handling a multitude of software threadssimultaneously. The CPU(s) 1006 may include any type of processor, andmay include different types of processors depending on the type ofcomputing device 1000 implemented (e.g., processors with fewer cores formobile devices and processors with more cores for servers). For example,depending on the type of computing device 1000, the processor may be anAdvanced RISC Machines (ARM) processor implemented using ReducedInstruction Set Computing (RISC) or an x86 processor implemented usingComplex Instruction Set Computing (CISC). The computing device 1000 mayinclude one or more CPUs 1006 in addition to one or more microprocessorsor supplementary co-processors, such as math co-processors.

In addition to or alternatively from the CPU(s) 1006, the GPU(s) 1008may be configured to execute at least some of the computer-readableinstructions to control one or more components of the computing device1000 to perform one or more of the methods and/or processes describedherein. One or more of the GPU(s) 1008 may be an integrated GPU (e.g.,with one or more of the CPU(s) 1006 and/or one or more of the GPU(s)1008 may be a discrete GPU. In embodiments, one or more of the GPU(s)1008 may be a coprocessor of one or more of the CPU(s) 1006. The GPU(s)1008 may be used by the computing device 1000 to render graphics (e.g.,3D graphics), to visually display of the hazard detection output fromthe stereo disparity machine learning hazard detector 102 onto the HMIdisplay 120, or perform general purpose computations. For example, theGPU(s) 1008 may be used for General-Purpose computing on GPUs (GPGPU).The GPU(s) 1008 may include hundreds or thousands of cores that arecapable of handling hundreds or thousands of software threadssimultaneously. The GPU(s) 1008 may generate pixel data for outputimages in response to rendering commands (e.g., rendering commands fromthe CPU(s) 1006 received via a host interface). The GPU(s) 1008 mayinclude graphics memory, such as display memory, for storing pixel dataor any other suitable data, such as GPGPU data. The display memory maybe included as part of the memory 1004. The GPU(s) 1008 may include twoor more GPUs operating in parallel (e.g., via a link). The link maydirectly connect the GPUs (e.g., using NVLINK) or may connect the GPUsthrough a switch (e.g., using NVSwitch). When combined together, eachGPU 1008 may generate pixel data or GPGPU data for different portions ofan output or for different outputs (e.g., a first GPU for a first imageand a second GPU for a second image). Each GPU may include its ownmemory, or may share memory with other GPUs.

In addition to or alternatively from the CPU(s) 1006 and/or the GPU(s)1008, the logic unit(s) 1020 may be configured to execute at least someof the computer-readable instructions to control one or more componentsof the computing device 1000 to perform one or more of the methodsand/or processes described herein. In embodiments, the CPU(s) 1006, theGPU(s) 1008, and/or the logic unit(s) 1020 may discretely or jointlyperform any combination of the methods, processes and/or portionsthereof. One or more of the logic units 1020 may be part of and/orintegrated in one or more of the CPU(s) 1006 and/or the GPU(s) 1008and/or one or more of the logic units 1020 may be discrete components orotherwise external to the CPU(s) 1006 and/or the GPU(s) 1008. Inembodiments, one or more of the logic units 1020 may be a coprocessor ofone or more of the CPU(s) 1006 and/or one or more of the GPU(s) 1008.

Examples of the logic unit(s) 1020 include one or more processing coresand/or components thereof, such as Data Processing Units (DPUs), TensorCores (TCs), Tensor Processing Units (TPUs), Pixel Visual Cores (PVCs),Vision Processing Units (VPUs), Graphics Processing Clusters (GPCs),Texture Processing Clusters (TPCs), Streaming Multiprocessors (SMs),Tree Traversal Units (TTUs), Artificial Intelligence Accelerators(AIAs), Deep Learning Accelerators (DLAs), Arithmetic-Logic Units(ALUs), Application-Specific Integrated Circuits (ASICs), Floating PointUnits (FPUs), input/output (I/O) elements, peripheral componentinterconnect (PCI) or peripheral component interconnect express (PCIe)elements, and/or the like.

The communication interface 1010 may include one or more receivers,transmitters, and/or transceivers that enable the computing device 1000to communicate with other computing devices via an electroniccommunication network, included wired and/or wireless communications.The communication interface 1010 may include components andfunctionality to enable communication over any of a number of differentnetworks, such as wireless networks (e.g., Wi-Fi, Z-Wave, Bluetooth,Bluetooth LE, ZigBee, etc.), wired networks (e.g., communicating overEthernet or InfiniBand), low-power wide-area networks (e.g., LoRaWAN,SigFox, etc.), and/or the Internet. In one or more embodiments, logicunit(s) 1020 and/or communication interface 1010 may include one or moredata processing units (DPUs) to transmit data received over a networkand/or through interconnect system 1002 directly to (e.g., a memory of)one or more GPU(s) 1008.

The I/O ports 1012 may enable the computing device 1000 to be logicallycoupled to other devices including the I/O components 1014, thepresentation component(s) 1018, and/or other components, some of whichmay be built in to (e.g., integrated in) the computing device 1000.Illustrative I/O components 1014 include a microphone, mouse, keyboard,joystick, game pad, game controller, satellite dish, scanner, printer,wireless device, etc. The I/O components 1014 may provide a natural userinterface (NUI) that processes air gestures, voice, or otherphysiological inputs generated by a user. In some instances, inputs maybe transmitted to an appropriate network element for further processing.An NUI may implement any combination of speech recognition, stylusrecognition, facial recognition, biometric recognition, gesturerecognition both on screen and adjacent to the screen, air gestures,head and eye tracking, and touch recognition (as described in moredetail below) associated with a display of the computing device 1000.The computing device 1000 may be include depth cameras, such asstereoscopic camera systems, infrared camera systems, RGB camerasystems, touchscreen technology, and combinations of these, for gesturedetection and recognition. Additionally, the computing device 1000 mayinclude accelerometers or gyroscopes (e.g., as part of an inertiameasurement unit (IMU)) that enable detection of motion. In someexamples, the output of the accelerometers or gyroscopes may be used bythe computing device 1000 to render immersive augmented reality orvirtual reality.

The power supply 1016 may include a hard-wired power supply, a batterypower supply, or a combination thereof. The power supply 1016 mayprovide power to the computing device 1000 to enable the components ofthe computing device 1000 to operate.

The presentation component(s) 1018 may include a display (e.g., amonitor, a touch screen, a television screen, a heads-up-display (HUD),other display types, or a combination thereof), speakers, and/or otherpresentation components. The presentation component(s) 1018 may receivedata from other components (e.g., the GPU(s) 1008, the CPU(s) 1006,DPUs, etc.), and output the data (e.g., as an image, video, sound,etc.).

Example Data Center

FIG. 11 illustrates an example data center 1100 that may be used in atleast one embodiments of the present disclosure. The data center 1100may include a data center infrastructure layer 1110, a framework layer1120, a software layer 1130, and/or an application layer 1140. Forexample, the stereo disparity machine learning hazard detector 102 mayexchange detected hazard information with the data center 1100.

As shown in FIG. 11 , the data center infrastructure layer 1110 mayinclude a resource orchestrator 1112, grouped computing resources 1114,and node computing resources (“node C.R.s”) 1116(1)-1116(N), where “N”represents any whole, positive integer. In at least one embodiment, nodeC.R.s 1116(1)-1116(N) may include, but are not limited to, any number ofcentral processing units (CPUs) or other processors (including DPUs,accelerators, field programmable gate arrays (FPGAs), graphicsprocessors or graphics processing units (GPUs), etc.), memory devices(e.g., dynamic read-only memory), storage devices (e.g., solid state ordisk drives), network input/output (NW I/O) devices, network switches,virtual machines (VMs), power modules, and/or cooling modules, etc. Insome embodiments, one or more node C.R.s from among node C.R.s1116(1)-1116(N) may correspond to a server having one or more of theabove-mentioned computing resources. In addition, in some embodiments,the node C.R.s 1116(1)-11161(N) may include one or more virtualcomponents, such as vGPUs, vCPUs, and/or the like, and/or one or more ofthe node C.R.s 1116(1)-1116(N) may correspond to a virtual machine (VM).

In at least one embodiment, grouped computing resources 1114 may includeseparate groupings of node C.R.s 1116 housed within one or more racks(not shown), or many racks housed in data centers at variousgeographical locations (also not shown). Separate groupings of nodeC.R.s 1116 within grouped computing resources 1114 may include groupedcompute, network, memory or storage resources that may be configured orallocated to support one or more workloads. In at least one embodiment,several node C.R.s 1116 including CPUs, GPUs, DPUs, and/or otherprocessors may be grouped within one or more racks to provide computeresources to support one or more workloads. The one or more racks mayalso include any number of power modules, cooling modules, and/ornetwork switches, in any combination.

The resource orchestrator 1112 may configure or otherwise control one ormore node C.R.s 1116(1)-1116(N) and/or grouped computing resources 1114.In at least one embodiment, resource orchestrator 1112 may include asoftware design infrastructure (SDI) management entity for the datacenter 1100. The resource orchestrator 1112 may include hardware,software, or some combination thereof.

In at least one embodiment, as shown in FIG. 11 , framework layer 1120may include a job scheduler 1132, a configuration manager 1134, aresource manager 1136, and/or a distributed file system 1138. Theframework layer 1120 may include a framework to support software 1132 ofsoftware layer 1130 and/or one or more application(s) 1142 ofapplication layer 1140. The software 1132 or application(s) 1142 mayrespectively include web-based service software or applications, such asthose provided by Amazon Web Services, Google Cloud and Microsoft Azure.The framework layer 1120 may be, but is not limited to, a type of freeand open-source software web application framework such as Apache Spark™(hereinafter “Spark”) that may utilize distributed file system 1138 forlarge-scale data processing (e.g., “big data”). In at least oneembodiment, job scheduler 1132 may include a Spark driver to facilitatescheduling of workloads supported by various layers of data center 1100.The configuration manager 1134 may be capable of configuring differentlayers such as software layer 1130 and framework layer 1120 includingSpark and distributed file system 1138 for supporting large-scale dataprocessing. The resource manager 1136 may be capable of managingclustered or grouped computing resources mapped to or allocated forsupport of distributed file system 1138 and job scheduler 1132. In atleast one embodiment, clustered or grouped computing resources mayinclude grouped computing resource 1114 at data center infrastructurelayer 1110. The resource manager 1136 may coordinate with resourceorchestrator 1112 to manage these mapped or allocated computingresources.

In at least one embodiment, software 1132 included in software layer1130 may include software used by at least portions of node C.R.s1116(1)-1116(N), grouped computing resources 1114, and/or distributedfile system 1138 of framework layer 1120. One or more types of softwaremay include, but are not limited to, Internet web page search software,e-mail virus scan software, database software, and streaming videocontent software.

In at least one embodiment, application(s) 1142 included in applicationlayer 1140 may include one or more types of applications used by atleast portions of node C.R.s 1116(1)-1116(N), grouped computingresources 1114, and/or distributed file system 1138 of framework layer1120. One or more types of applications may include, but are not limitedto, any number of a genomics application, a cognitive compute, and amachine learning application, including training or inferencingsoftware, machine learning framework software (e.g., PyTorch,TensorFlow, Caffe, etc.), and/or other machine learning applicationsused in conjunction with one or more embodiments.

In at least one embodiment, any of configuration manager 1134, resourcemanager 1136, and resource orchestrator 1112 may implement any numberand type of self-modifying actions based on any amount and type of dataacquired in any technically feasible fashion. Self-modifying actions mayrelieve a data center operator of data center 1100 from making possiblybad configuration decisions and possibly avoiding underutilized and/orpoor performing portions of a data center.

The data center 1100 may include tools, services, software or otherresources to train one or more machine learning models or predict orinfer information using one or more machine learning models according toone or more embodiments described herein. For example, a machinelearning model(s) may be trained by calculating weight parametersaccording to a neural network architecture using software and/orcomputing resources described above with respect to the data center1100. In at least one embodiment, trained or deployed machine learningmodels corresponding to one or more neural networks may be used to inferor predict information using resources described above with respect tothe data center 1100 by using weight parameters calculated through oneor more training techniques, such as but not limited to those describedherein.

In at least one embodiment, the data center 1100 may use CPUs,application-specific integrated circuits (ASICs), GPUs, FPGAs, and/orother hardware (or virtual compute resources corresponding thereto) toperform training and/or inferencing using above-described resources.Moreover, one or more software and/or hardware resources described abovemay be configured as a service to allow users to train or performinginferencing of information, such as image recognition, speechrecognition, or other artificial intelligence services.

Example Network Environments

Network environments suitable for use in implementing embodiments of thedisclosure may include one or more client devices, servers, networkattached storage (NAS), other backend devices, and/or other devicetypes. The client devices, servers, and/or other device types (e.g.,each device) may be implemented on one or more instances of thecomputing device(s) 1000 of FIG. 10 —e.g., each device may includesimilar components, features, and/or functionality of the computingdevice(s) 1000. In addition, where backend devices (e.g., servers, NAS,etc.) are implemented, the backend devices may be included as part of adata center 1100, an example of which is described in more detail hereinwith respect to FIG. 11 .

Components of a network environment may communicate with each other viaa network(s), which may be wired, wireless, or both. The network mayinclude multiple networks, or a network of networks. By way of example,the network may include one or more Wide Area Networks (WANs), one ormore Local Area Networks (LANs), one or more public networks such as theInternet and/or a public switched telephone network (PSTN), and/or oneor more private networks. Where the network includes a wirelesstelecommunications network, components such as a base station, acommunications tower, or even access points (as well as othercomponents) may provide wireless connectivity.

Compatible network environments may include one or more peer-to-peernetwork environments—in which case a server may not be included in anetwork environment—and one or more client-server networkenvironments—in which case one or more servers may be included in anetwork environment. In peer-to-peer network environments, functionalitydescribed herein with respect to a server(s) may be implemented on anynumber of client devices.

In at least one embodiment, a network environment may include one ormore cloud-based network environments, a distributed computingenvironment, a combination thereof, etc. A cloud-based networkenvironment may include a framework layer, a job scheduler, a resourcemanager, and a distributed file system implemented on one or more ofservers, which may include one or more core network servers and/or edgeservers. A framework layer may include a framework to support softwareof a software layer and/or one or more application(s) of an applicationlayer. The software or application(s) may respectively include web-basedservice software or applications. In embodiments, one or more of theclient devices may use the web-based service software or applications(e.g., by accessing the service software and/or applications via one ormore application programming interfaces (APIs)). The framework layer maybe, but is not limited to, a type of free and open-source software webapplication framework such as that may use a distributed file system forlarge-scale data processing (e.g., “big data”).

A cloud-based network environment may provide cloud computing and/orcloud storage that carries out any combination of computing and/or datastorage functions described herein (or one or more portions thereof).Any of these various functions may be distributed over multiplelocations from central or core servers (e.g., of one or more datacenters that may be distributed across a state, a region, a country, theglobe, etc.). If a connection to a user (e.g., a client device) isrelatively close to an edge server(s), a core server(s) may designate atleast a portion of the functionality to the edge server(s). Acloud-based network environment may be private (e.g., limited to asingle organization), may be public (e.g., available to manyorganizations), and/or a combination thereof (e.g., a hybrid cloudenvironment).

The client device(s) may include at least some of the components,features, and functionality of the example computing device(s) 1000described herein with respect to FIG. 10 . By way of example and notlimitation, a client device may be embodied as a Personal Computer (PC),a laptop computer, a mobile device, a smartphone, a tablet computer, asmart watch, a wearable computer, a Personal Digital Assistant (PDA), anMP3 player, a virtual reality headset, a Global Positioning System (GPS)or device, a video player, a video camera, a surveillance device orsystem, a vehicle, a boat, a flying vessel, a virtual machine, a drone,a robot, a handheld communications device, a hospital device, a gamingdevice or system, an entertainment system, a vehicle computer system, anembedded system controller, a remote control, an appliance, a consumerelectronic device, a workstation, an edge device, any combination ofthese delineated devices, or any other suitable device.

The disclosure may be described in the general context of computer codeor machine-useable instructions, including computer-executableinstructions such as program modules, being executed by a computer orother machine, such as a personal data assistant or other handhelddevice. Generally, program modules including routines, programs,objects, components, data structures, etc., refer to code that performparticular tasks or implement particular abstract data types. Thedisclosure may be practiced in a variety of system configurations,including hand-held devices, consumer electronics, general-purposecomputers, more specialty computing devices, etc. The disclosure mayalso be practiced in distributed computing environments where tasks areperformed by remote-processing devices that are linked through acommunications network.

As used herein, a recitation of “and/or” with respect to two or moreelements should be interpreted to mean only one element, or acombination of elements. For example, “element A, element B, and/orelement C” may include only element A, only element B, only element C,element A and element B, element A and element C, element B and elementC, or elements A, B, and C. In addition, “at least one of element A orelement B” may include at least one of element A, at least one ofelement B, or at least one of element A and at least one of element B.Further, “at least one of element A and element B” may include at leastone of element A, at least one of element B, or at least one of elementA and at least one of element B.

The subject matter of the present disclosure is described withspecificity herein to meet statutory requirements. However, thedescription itself is not intended to limit the scope of thisdisclosure. Rather, the inventors have contemplated that the claimedsubject matter might also be embodied in other ways, to includedifferent steps or combinations of steps similar to the ones describedin this document, in conjunction with other present or futuretechnologies. Moreover, although the terms “step” and/or “block” may beused herein to connote different elements of methods employed, the termsshould not be interpreted as implying any particular order among orbetween various steps herein disclosed unless and except when the orderof individual steps is explicitly described.

What is claimed is:
 1. A processor comprising: one or more circuits to:generate a disparity map indicative of disparities between a first imagegenerated using a first sensor and a second image generated using asecond sensor, the first sensor and the second sensor having at leastpartially overlapping fields of view including at least a portion of apath of an ego-machine; generate a path disparity model based at leastin part on the disparity map; compute, using one or more machinelearning models, one or more first feature vectors corresponding to thefirst image and one or more second feature vectors corresponding to thesecond image; determine, based at least in part on the path disparitymodel, a similarity between a first feature vector of the one or morefirst feature vectors and a second feature vector of the one or moresecond feature vectors that is associated with the first feature vectorbased at least in part on the path disparity model; classify one or morepixels of at least one of the first image or the second image ascorresponding to one or more hazards based at least in part on the firstfeature vector and the second feature vector; and perform one or moreoperations for controlling the ego-machine along the path based at leastin part on the one or more hazards.
 2. The processor of claim 1, whereinthe one or more pixels are classified using the one or more machinelearning models or one or more other machine learning models.
 3. Theprocessor of claim 1, wherein one or more of the first feature vector,the second feature vector, and the classification of the one or morepixels is computed using the one or more machine learning models, andthe one or more machine learning models are trained using end-to-endtraining.
 4. The processor of claim 1, wherein the one or more circuitsare further to: classify one or more other pixels of at least one of thefirst image and the second image as corresponding to the one or morehazards; apply a clustering algorithm to the one or more pixels and theone or more other pixels to generate one or more clusters of pixels; anddefine one or more locations of the one or more hazards using the one ormore clusters of pixels.
 5. The processor of claim 4, wherein the one ormore locations are defined by generating one or more bounding shapesaround at least one respective cluster of pixels of the one or moreclusters of pixels.
 6. The processor of claim 1, wherein the one or morepixels is classified using a combined feature vector, the combinedfeature vector being generated using the path disparity model, the firstfeature vector, and the second feature vector.
 7. The processor of claim1, wherein the one or more first feature vectors include a featurevector for at least one pixel of the first image within a region ofinterest and the one or more second feature vectors include a featurevector for at least one pixel of the second image.
 8. The processor ofclaim 1, wherein the one or more machine learning models are trainedusing ground truth data that comprises classification labelscorresponding to a drivable region, a non-drivable region, and hazards.9. The processor of claim 8, wherein a delineation of the non-drivableregion from the drivable region is determined using freespaceinformation.
 10. The processor of claim 1, wherein the path disparitymodel is represented in a disparity space including a first axiscorresponding to disparity and a second axis corresponding to an imagerow of at least one of the first image or the second image.
 11. Theprocessor of claim 1, wherein the generation of the path disparity modelincludes applying a line fitting algorithm to at least a portion of thedisparity map.
 12. The processor of claim 1, wherein the processor iscomprised in at least one of: a control system for an autonomous orsemi-autonomous machine; a perception system for an autonomous orsemi-autonomous machine; a system for performing simulation operations;a system for performing digital twin operations; a system for performinglight transport simulation; a system for performing collaborativecontent creation for 3D assets; a system for performing deep learningoperations; a system implemented using an edge device; a systemimplemented using a robot; a system for performing conversational AIoperations; a system for generating synthetic data; a systemincorporating one or more virtual machines (VMs); a system implementedat least partially in a data center; or a system implemented at leastpartially using cloud computing resources.
 13. A system comprising: aplurality of sensors to generate sensor data; one or more processingunits comprising processing circuitry to: generate a disparity mapindicative of disparities between a pair of stereo images generatedusing the plurality of sensors, the plurality of sensors comprising afirst sensor and a second sensor having at least partially overlappingfields of view including at least a portion of a path of an ego-machine;generate a path disparity model at least in part from the disparity map;compute, using one or more machine learning models, at least one firstfeature vector from a first image of the pair of stereo images and atleast one second feature vector from a second image of the pair ofstereo images; apply the path disparity model to determine anassociation between the at least one first feature vector and the atleast one second feature vector; classify one or more pixels of the pairof stereo images as a hazard pixel corresponding to a hazard object onthe path based at least in part on the association between the at leastone first feature vector and the at least one second feature vector;generate an output indicating a presence of the hazard object on thepath of the ego-machine.
 14. The system of claim 13, wherein theprocessing circuitry is further to: cluster the one or more pixelsclassified as the hazard pixel to generate one or more clusters ofpixels; and define a location of the hazard object using the one or moreclusters of pixels.
 15. The system of claim 13, wherein thedetermination of the association between the at least one first featurevector and the at least one second feature vector is performed, at leastin part, by projecting the at least one first feature vector into thesecond image at a pixel location determined based at least in part onthe path disparity model.
 16. The system of claim 13, wherein theassociation between the at least one first feature vector and the atleast one second feature vector is determined using the one or moremachine learning models or one or more other machine learning models.17. The system of claim 13, wherein the generation of the path disparitymodel includes applying a line fitting algorithm to at least a portionof the disparity map.
 18. The system of claim 13, wherein theassociation and the classification of the one or more pixels isdetermined using the one or more machine learning models, and the one ormore machine learning models are trained using end-to-end training. 19.The system of claim 13, wherein the one or more pixels is classifiedusing a combined feature vector, the combined feature vector beinggenerated using the association and the at least one first featurevector and the at least one second feature vector.
 20. The system ofclaim 13, wherein the one or more machine learning models are trainedusing ground truth data that comprises classification labelscorresponding to a drivable region, a non-drivable region, and hazards.21. The system of claim 13, wherein the processing circuitry filters thepair of stereo images to a region of interest that includes the path ofthe ego-machine.
 22. The system of claim 13, wherein the system iscomprised in at least one of: a control system for an autonomous orsemi-autonomous machine; a perception system for an autonomous orsemi-autonomous machine; a system for performing simulation operations;a system for performing digital twin operations; a system for performinglight transport simulation; a system for performing collaborativecontent creation for 3D assets; a system for performing deep learningoperations; a system implemented using an edge device; a systemimplemented using a robot; a system for performing conversational AIoperations; a system for generating synthetic data; a systemincorporating one or more virtual machines (VMs); a system implementedat least partially in a data center; or a system implemented at leastpartially using cloud computing resources.
 23. A method comprising:determining a location of one or more pixels corresponding to one ormore hazard objects on a path of an ego-machine based at least in parton classifying the one or more pixels using a first feature vectorcorresponding to a first image and a second feature vector correspondingto a second image, the first feature vector and the second featurevector determined to correspond to one another based at least in part ona path disparity model computed using a disparity map generated usingthe first image and the second image.